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href="https://www.academia.edu/123437086/RTP_LX_Can_LLMs_Evaluate_Toxicity_in_Multilingual_Scenarios"><img alt="Research paper thumbnail of RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?" class="work-thumbnail" src="https://attachments.academia-assets.com/117867625/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123437086/RTP_LX_Can_LLMs_Evaluate_Toxicity_in_Multilingual_Scenarios">RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Apr 22, 2024</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="84e19e981f22013693f448b6ac3a5ecb" class="wp-workCard--action" rel="nofollow" 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class="js-work-more-abstract-truncated">This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (L...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="41d947d0043c83d554f34379954427b2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190331"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190331/SemEval_2019_Task_6_Identifying_and_Categorizing_Offensive_Language_in_Social_Media_OffensEval_"><img alt="Research paper thumbnail of SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)" class="work-thumbnail" src="https://attachments.academia-assets.com/92211251/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190331/SemEval_2019_Task_6_Identifying_and_Categorizing_Offensive_Language_in_Social_Media_OffensEval_">SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)</a></div><div class="wp-workCard_item"><span>Proceedings of the 13th International Workshop on Semantic Evaluation</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7514d4492187f7cd5fb3e33775080066" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211251,"asset_id":88190331,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211251/download_file?st=MTczMjQ5NTI0OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" 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The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report. 1 http://competitions.codalab.org/ competitions/20011 2 http://scholar.harvard.edu/malmasi/ olid 3 A total of 800 teams signed up to participate in the task, but only 115 teams ended up submitting results eventually.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of the 13th International Workshop on Semantic Evaluation","grobid_abstract_attachment_id":92211251},"translated_abstract":null,"internal_url":"https://www.academia.edu/88190331/SemEval_2019_Task_6_Identifying_and_Categorizing_Offensive_Language_in_Social_Media_OffensEval_","translated_internal_url":"","created_at":"2022-10-09T15:29:02.092-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":794499,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":92211251,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211251/thumbnails/1.jpg","file_name":"1903.pdf","download_url":"https://www.academia.edu/attachments/92211251/download_file?st=MTczMjQ5NTI0OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"SemEval_2019_Task_6_Identifying_and_Cate.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211251/1903-libre.pdf?1665355131=\u0026response-content-disposition=attachment%3B+filename%3DSemEval_2019_Task_6_Identifying_and_Cate.pdf\u0026Expires=1732498848\u0026Signature=Yxm1BENjAVyoSDDyJ5uM4GI8fTbMzGPl3vdy74Ck-2cnUp6ZdP3iep~HavTFPQfMmlTBa2qom~fWWWZK38XE1AhTlWICBjo8ag4R-hKz5ist9d~Zlqo-L26I3bCKm96CU06e6AKnzwUbBSqf2BuBr7zgI1QupOWoHKa33YK73OOIWmjtPrQjePnphTesfV6JDoLSe1mK4bkCRP~nWliqaAh~VImF-pEreKkOHjyLVL8P2x5DW2YpVVh853CJ~2s2x~DIDre7oA77ya08K-5VTDPpoJXlhAwVZ5xD6AtXJhN9i2Y0aP21rbne7ZlpyfSxOqZoYR6-Eo6IeCY2~dUgVg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"SemEval_2019_Task_6_Identifying_and_Categorizing_Offensive_Language_in_Social_Media_OffensEval_","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":794499,"first_name":"Noura","middle_initials":null,"last_name":"Farra","page_name":"NouraFarra","domain_name":"columbia","created_at":"2011-09-27T22:02:49.992-07:00","display_name":"Noura Farra","url":"https://columbia.academia.edu/NouraFarra"},"attachments":[{"id":92211251,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211251/thumbnails/1.jpg","file_name":"1903.pdf","download_url":"https://www.academia.edu/attachments/92211251/download_file?st=MTczMjQ5NTI0OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"SemEval_2019_Task_6_Identifying_and_Cate.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211251/1903-libre.pdf?1665355131=\u0026response-content-disposition=attachment%3B+filename%3DSemEval_2019_Task_6_Identifying_and_Cate.pdf\u0026Expires=1732498848\u0026Signature=Yxm1BENjAVyoSDDyJ5uM4GI8fTbMzGPl3vdy74Ck-2cnUp6ZdP3iep~HavTFPQfMmlTBa2qom~fWWWZK38XE1AhTlWICBjo8ag4R-hKz5ist9d~Zlqo-L26I3bCKm96CU06e6AKnzwUbBSqf2BuBr7zgI1QupOWoHKa33YK73OOIWmjtPrQjePnphTesfV6JDoLSe1mK4bkCRP~nWliqaAh~VImF-pEreKkOHjyLVL8P2x5DW2YpVVh853CJ~2s2x~DIDre7oA77ya08K-5VTDPpoJXlhAwVZ5xD6AtXJhN9i2Y0aP21rbne7ZlpyfSxOqZoYR6-Eo6IeCY2~dUgVg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":544669,"name":"Language Identification","url":"https://www.academia.edu/Documents/in/Language_Identification"},{"id":663814,"name":"Offensive Realism","url":"https://www.academia.edu/Documents/in/Offensive_Realism"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190330"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190330/Cross_lingual_sentiment_transfer_with_limited_resources"><img alt="Research paper thumbnail of Cross-lingual sentiment transfer with limited resources" class="work-thumbnail" src="https://attachments.academia-assets.com/92211255/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190330/Cross_lingual_sentiment_transfer_with_limited_resources">Cross-lingual sentiment transfer with limited resources</a></div><div class="wp-workCard_item"><span>Machine Translation</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="96d4583084ff63d6aeb8d223f05b8acf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211255,"asset_id":88190330,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211255/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190330"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190330"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190330; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190329"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190329/Predicting_the_Type_and_Target_of_Offensive_Posts_in_Social_Media"><img alt="Research paper thumbnail of Predicting the Type and Target of Offensive Posts in Social Media" class="work-thumbnail" src="https://attachments.academia-assets.com/92211249/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190329/Predicting_the_Type_and_Target_of_Offensive_Posts_in_Social_Media">Predicting the Type and Target of Offensive Posts in Social Media</a></div><div class="wp-workCard_item"><span>Proceedings of the 2019 Conference of the North</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a4f65f8d0a5847f88e70379b669d9604" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211249,"asset_id":88190329,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211249/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190329"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190329"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190329; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88190329]").text(description); $(".js-view-count[data-work-id=88190329]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88190329; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88190329']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88190329, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190328"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190328/SemEval_2017_Task_4_Sentiment_Analysis_in_Twitter"><img alt="Research paper thumbnail of SemEval-2017 Task 4: Sentiment Analysis in Twitter" class="work-thumbnail" src="https://attachments.academia-assets.com/92211250/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190328/SemEval_2017_Task_4_Sentiment_Analysis_in_Twitter">SemEval-2017 Task 4: Sentiment Analysis in Twitter</a></div><div class="wp-workCard_item"><span>Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6dfad8e0f3b05e71da6c270449a71851" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211250,"asset_id":88190328,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211250/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190328; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190326"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190326/Annotating_Targets_of_Opinions_in_Arabic_using_Crowdsourcing"><img alt="Research paper thumbnail of Annotating Targets of Opinions in Arabic using Crowdsourcing" class="work-thumbnail" src="https://attachments.academia-assets.com/92211248/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190326/Annotating_Targets_of_Opinions_in_Arabic_using_Crowdsourcing">Annotating Targets of Opinions in Arabic using Crowdsourcing</a></div><div class="wp-workCard_item"><span>Proceedings of the Second Workshop on Arabic Natural Language Processing</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b9719ba17b93796f34f47ac31ec081b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211248,"asset_id":88190326,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211248/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190326"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190326"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190326; 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The first stage consists of identifying candidate targets \"entities\" in a given text. The second stage consists of identifying the opinion polarity (positive, negative, or neutral) expressed about a specific entity. We annotate a corpus of Arabic text using this method, selecting our data from online commentaries in different domains. Despite the complexity of the task, we find high agreement. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190325"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190325/The_Columbia_System_in_the_QALB_2014_Shared_Task_on_Arabic_Error_Correction"><img alt="Research paper thumbnail of The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction" class="work-thumbnail" src="https://attachments.academia-assets.com/92211247/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190325/The_Columbia_System_in_the_QALB_2014_Shared_Task_on_Arabic_Error_Correction">The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction</a></div><div class="wp-workCard_item"><span>Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3cb58e72e5df15f45efeec38e8b844b1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211247,"asset_id":88190325,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211247/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190325"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190325"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190325; 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In this paper, we describe the Columbia University entry in the shared task. Our system consists of several components that rely on machinelearning techniques and linguistic knowledge. We submitted three versions of the system: these share several core elements but each version also includes additional components. We describe our underlying approach and the special aspects of the different versions of our submission. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190324"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190324/Large_Scale_Arabic_Error_Annotation_Guidelines_and_Framework"><img alt="Research paper thumbnail of Large Scale Arabic Error Annotation: Guidelines and Framework" class="work-thumbnail" src="https://attachments.academia-assets.com/92211253/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190324/Large_Scale_Arabic_Error_Annotation_Guidelines_and_Framework">Large Scale Arabic Error Annotation: Guidelines and Framework</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present annotation guidelines and a web-based annotation framework developed as part of an eff...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2525ba2ad785c782e6de0fe3718a4ab0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211253,"asset_id":88190324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211253/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190324"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88190324]").text(description); $(".js-view-count[data-work-id=88190324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88190324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88190324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88190324, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2525ba2ad785c782e6de0fe3718a4ab0" } } $('.js-work-strip[data-work-id=88190324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88190324,"title":"Large Scale Arabic Error Annotation: Guidelines and Framework","translated_title":"","metadata":{"abstract":"We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations."},"translated_abstract":"We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.","internal_url":"https://www.academia.edu/88190324/Large_Scale_Arabic_Error_Annotation_Guidelines_and_Framework","translated_internal_url":"","created_at":"2022-10-09T15:29:01.025-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":794499,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":92211253,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211253/thumbnails/1.jpg","file_name":"ZAGHOUANI14.956.L14-1721.pdf","download_url":"https://www.academia.edu/attachments/92211253/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Large_Scale_Arabic_Error_Annotation_Guid.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211253/ZAGHOUANI14.956.L14-1721-libre.pdf?1665355125=\u0026response-content-disposition=attachment%3B+filename%3DLarge_Scale_Arabic_Error_Annotation_Guid.pdf\u0026Expires=1732498849\u0026Signature=Pc5XDmi2HsHzNDwDKCDRIa29tT72nbzMmU1fME7BZIvCjJm~HeqokUtIu5qyRUPpSSwCYh4XT8N41VsWh8QZKMraabeSltVKBZ24ej7rGIXtHF1n5KpEPVroc6G3z-i9rLlTAd40-aoWI8xUUE9SFejd3AhXX7-~qOMBu1v5T-~tlOglJVPqEUW90FZudqZ-faUj-6AWjAiZ1puVDNtDyQgvWUsumlYmHSY-ydp6-VBxiMXOFt0AlX7GofV2myF2aoiCeYW0Uzwt8-lrDb5RHdWg92LuVumcXyu8iIU08O~FyE7mVXMMyhaOeH6YdjE-nJoa-OgC5wnNtB~MYxq7pQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Large_Scale_Arabic_Error_Annotation_Guidelines_and_Framework","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":794499,"first_name":"Noura","middle_initials":null,"last_name":"Farra","page_name":"NouraFarra","domain_name":"columbia","created_at":"2011-09-27T22:02:49.992-07:00","display_name":"Noura Farra","url":"https://columbia.academia.edu/NouraFarra"},"attachments":[{"id":92211253,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211253/thumbnails/1.jpg","file_name":"ZAGHOUANI14.956.L14-1721.pdf","download_url":"https://www.academia.edu/attachments/92211253/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Large_Scale_Arabic_Error_Annotation_Guid.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211253/ZAGHOUANI14.956.L14-1721-libre.pdf?1665355125=\u0026response-content-disposition=attachment%3B+filename%3DLarge_Scale_Arabic_Error_Annotation_Guid.pdf\u0026Expires=1732498849\u0026Signature=Pc5XDmi2HsHzNDwDKCDRIa29tT72nbzMmU1fME7BZIvCjJm~HeqokUtIu5qyRUPpSSwCYh4XT8N41VsWh8QZKMraabeSltVKBZ24ej7rGIXtHF1n5KpEPVroc6G3z-i9rLlTAd40-aoWI8xUUE9SFejd3AhXX7-~qOMBu1v5T-~tlOglJVPqEUW90FZudqZ-faUj-6AWjAiZ1puVDNtDyQgvWUsumlYmHSY-ydp6-VBxiMXOFt0AlX7GofV2myF2aoiCeYW0Uzwt8-lrDb5RHdWg92LuVumcXyu8iIU08O~FyE7mVXMMyhaOeH6YdjE-nJoa-OgC5wnNtB~MYxq7pQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":38072,"name":"Annotation","url":"https://www.academia.edu/Documents/in/Annotation"},{"id":49267,"name":"Computational Linguistics \u0026 NLP","url":"https://www.academia.edu/Documents/in/Computational_Linguistics_and_NLP"},{"id":235465,"name":"Guidelines","url":"https://www.academia.edu/Documents/in/Guidelines"},{"id":630181,"name":"Error Annotation","url":"https://www.academia.edu/Documents/in/Error_Annotation"},{"id":3425866,"name":"Corpus Compilation","url":"https://www.academia.edu/Documents/in/Corpus_Compilation"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190323"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190323/Generalized_Character_Level_Spelling_Error_Correction"><img alt="Research paper thumbnail of Generalized Character-Level Spelling Error Correction" class="work-thumbnail" src="https://attachments.academia-assets.com/92211254/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190323/Generalized_Character_Level_Spelling_Error_Correction">Generalized Character-Level Spelling Error Correction</a></div><div class="wp-workCard_item"><span>Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cf692fa42044b275c8dc035a87686fe1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211254,"asset_id":88190323,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211254/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190323"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190323"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190323; 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While operating at the character level, the model makes use of wordlevel and contextual information. In contrast to previous work, the proposed approach learns to correct a variety of error types without guidance of manuallyselected constraints or language-specific features. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190322"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190322/A_Mobile_Sensing_and_Imaging_System_for_Real_Time_Monitoring_of_Spine_Health"><img alt="Research paper thumbnail of A Mobile Sensing and Imaging System for Real-Time Monitoring of Spine Health" class="work-thumbnail" src="https://attachments.academia-assets.com/92211245/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190322/A_Mobile_Sensing_and_Imaging_System_for_Real_Time_Monitoring_of_Spine_Health">A Mobile Sensing and Imaging System for Real-Time Monitoring of Spine Health</a></div><div class="wp-workCard_item"><span>Journal of Medical Imaging and Health Informatics</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="411568d475c7bffe936eb9063f7756c8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211245,"asset_id":88190322,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211245/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190322"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190322"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190322; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88190322]").text(description); $(".js-view-count[data-work-id=88190322]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88190322; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88190322']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88190322, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "411568d475c7bffe936eb9063f7756c8" } } $('.js-work-strip[data-work-id=88190322]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88190322,"title":"A Mobile Sensing and Imaging System for Real-Time Monitoring of Spine Health","translated_title":"","metadata":{"publisher":"American Scientific Publishers","grobid_abstract":"Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. Spine stress can also worsen existing structural deformities in the spine such as adolescent idiopathic scoliosis (AIS). In this work we developed a system to monitor spine health through both dynamic monitoring and structural imaging. The dynamic sensing method monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing, and provides real-time user feedback when poor posture is sustained. The imaging method extracts the structural curvature of the spine and is used for the diagnosis of AIS in a non-invasive and inexpensive manner. Namely, the image is obtained using a photograph where the spinous processes have been marked to trace the shape of the spine. The spine curvature is then extracted automatically and modeled by a curve-fitting polynomial. The approach is simple and practical and allows scoliosis patients to monitor their curvature progress from home while minimizing the use of X-rays. The theme of our work is spine health, which we monitor through the wireless sensing system and the orthopedic imaging system. The two are complementary: the mobile wireless system assesses spine health during daily activity while the imaging system can assess the progression of a patient's structural spine curvature. We demonstrate effectiveness of our sensing system in simultaneously monitoring posture and position by testing in numerous situations. Furthermore, experiments show that our imaging method is accurate and robust under different brightness conditions. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190320"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190320/Energy_efficient_mobile_gesture_recognition_with_computation_offloading"><img alt="Research paper thumbnail of Energy-efficient mobile gesture recognition with computation offloading" class="work-thumbnail" src="https://attachments.academia-assets.com/92211246/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190320/Energy_efficient_mobile_gesture_recognition_with_computation_offloading">Energy-efficient mobile gesture recognition with computation offloading</a></div><div class="wp-workCard_item"><span>2011 International Conference on Energy Aware Computing</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e2f9559823aa27b2151056674fce1f47" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211246,"asset_id":88190320,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211246/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190320"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190320"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190320; 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Continuous gesture recognition places stringent demands on device power consumption, battery life and processing capability. In this work, we show that we can reduce the energy consumed during continuous gesture recognition on a mobile device with the delegation of the pre-processing stages, which filter out non-gesture segments, to a low power node that is separate from the main CPU. The main CPU can thus be kept in stop mode until a potential gesture is detected by the low power node, invoking the main processor to perform the computationintensive gesture classification to detect which exact gesture has been performed by the user. We present details of the processing performance and power consumed at each step of the processing pipeline, showing the extent of power savings achieved. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5011071"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5011071/A_novel_mobile_wireless_sensing_system_for_realtime_monitoring_of_posture_and_spine_stress"><img alt="Research paper thumbnail of A novel mobile wireless sensing system for realtime monitoring of posture and spine stress" class="work-thumbnail" src="https://attachments.academia-assets.com/34618863/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5011071/A_novel_mobile_wireless_sensing_system_for_realtime_monitoring_of_posture_and_spine_stress">A novel mobile wireless sensing system for realtime monitoring of posture and spine stress</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. We have developed a novel wireless mobile sensing system which monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing. The system provides a new method of measuring spine stress at both the back and the feet by integrating posture sensors with strain sensors. Posture and strain data is collected by means of a posture sensor at the neck and weight sensors at the feet. Data is transmitted wirelessly to a central processing station and real-time feedback is provided to the user's mobile device when sustained bad posture is detected. Moreover, the position of the patient (sitting, standing, or walking) can be determined by analysis of the weight sensor data and is visualized in real-time, along with back posture, at the central station by means of a graphical animation. Finally, data from all sensors is stored in a database to enable post processing and data analysis, and a summary report of daily posture and physical activity is sent to the user's email. The use of centralized processing allows for high performance data analysis and storage at the central station which enables tracking of the individual's progress. We demonstrate effectiveness of our system in simultaneously monitoring posture and position by testing in numerous situations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8ff7300afb45599012c91458e83ba61c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34618863,"asset_id":5011071,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34618863/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5011071"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5011071"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5011071; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5011071]").text(description); $(".js-view-count[data-work-id=5011071]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5011071; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='5011071']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 5011071, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8ff7300afb45599012c91458e83ba61c" } } $('.js-work-strip[data-work-id=5011071]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":5011071,"title":"A novel mobile wireless sensing system for realtime monitoring of posture and spine stress","translated_title":"","metadata":{"abstract":"Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. We have developed a novel wireless mobile sensing system which monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing. The system provides a new method of measuring spine stress at both the back and the feet by integrating posture sensors with strain sensors. Posture and strain data is collected by means of a posture sensor at the neck and weight sensors at the feet. Data is transmitted wirelessly to a central processing station and real-time feedback is provided to the user's mobile device when sustained bad posture is detected. Moreover, the position of the patient (sitting, standing, or walking) can be determined by analysis of the weight sensor data and is visualized in real-time, along with back posture, at the central station by means of a graphical animation. Finally, data from all sensors is stored in a database to enable post processing and data analysis, and a summary report of daily posture and physical activity is sent to the user's email. The use of centralized processing allows for high performance data analysis and storage at the central station which enables tracking of the individual's progress. We demonstrate effectiveness of our system in simultaneously monitoring posture and position by testing in numerous situations.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}}},"translated_abstract":"Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. We have developed a novel wireless mobile sensing system which monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing. The system provides a new method of measuring spine stress at both the back and the feet by integrating posture sensors with strain sensors. Posture and strain data is collected by means of a posture sensor at the neck and weight sensors at the feet. Data is transmitted wirelessly to a central processing station and real-time feedback is provided to the user's mobile device when sustained bad posture is detected. Moreover, the position of the patient (sitting, standing, or walking) can be determined by analysis of the weight sensor data and is visualized in real-time, along with back posture, at the central station by means of a graphical animation. Finally, data from all sensors is stored in a database to enable post processing and data analysis, and a summary report of daily posture and physical activity is sent to the user's email. The use of centralized processing allows for high performance data analysis and storage at the central station which enables tracking of the individual's progress. We demonstrate effectiveness of our system in simultaneously monitoring posture and position by testing in numerous situations.","internal_url":"https://www.academia.edu/5011071/A_novel_mobile_wireless_sensing_system_for_realtime_monitoring_of_posture_and_spine_stress","translated_internal_url":"","created_at":"2013-11-06T03:51:37.141-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":794499,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":34618863,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/34618863/thumbnails/1.jpg","file_name":"posture.pdf","download_url":"https://www.academia.edu/attachments/34618863/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_novel_mobile_wireless_sensing_system_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/34618863/posture-libre.pdf?1409770627=\u0026response-content-disposition=attachment%3B+filename%3DA_novel_mobile_wireless_sensing_system_f.pdf\u0026Expires=1732498849\u0026Signature=Gt0sJUWf9tf64QpyT8p5rkSV0LsRGJwIJb6hddMekjKq83A6yMCQfmsSv5SuR1fk~mm8zw4PQbEu8~K-JYIapTz60k6Ijpgu9gZiXyi3SxXaXlyd534uWP5G5p-vqAhBLzDfM70NruoO6GmOhRoON4YZ6Ixww-CtWgJbUGcRkC1WAUu2aHSJynMLrdO4mTe58ZAaeEwETldg-gavCaknqhAZ8l2CMltU0R7BxunsGcKRY93vr5dNSnPEENWEx~SlrR7ECC0oATR~iDshdMxXgUrVU8GuNEGhJkHSRu~7w4sHNo019IcSaSbWxeTwkxMvLnHCdjMSW9vpngyvyVzFMw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_novel_mobile_wireless_sensing_system_for_realtime_monitoring_of_posture_and_spine_stress","translated_slug":"","page_count":4,"language":"en","content_type":"Work","owner":{"id":794499,"first_name":"Noura","middle_initials":null,"last_name":"Farra","page_name":"NouraFarra","domain_name":"columbia","created_at":"2011-09-27T22:02:49.992-07:00","display_name":"Noura Farra","url":"https://columbia.academia.edu/NouraFarra"},"attachments":[{"id":34618863,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/34618863/thumbnails/1.jpg","file_name":"posture.pdf","download_url":"https://www.academia.edu/attachments/34618863/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_novel_mobile_wireless_sensing_system_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/34618863/posture-libre.pdf?1409770627=\u0026response-content-disposition=attachment%3B+filename%3DA_novel_mobile_wireless_sensing_system_f.pdf\u0026Expires=1732498849\u0026Signature=Gt0sJUWf9tf64QpyT8p5rkSV0LsRGJwIJb6hddMekjKq83A6yMCQfmsSv5SuR1fk~mm8zw4PQbEu8~K-JYIapTz60k6Ijpgu9gZiXyi3SxXaXlyd534uWP5G5p-vqAhBLzDfM70NruoO6GmOhRoON4YZ6Ixww-CtWgJbUGcRkC1WAUu2aHSJynMLrdO4mTe58ZAaeEwETldg-gavCaknqhAZ8l2CMltU0R7BxunsGcKRY93vr5dNSnPEENWEx~SlrR7ECC0oATR~iDshdMxXgUrVU8GuNEGhJkHSRu~7w4sHNo019IcSaSbWxeTwkxMvLnHCdjMSW9vpngyvyVzFMw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":3132,"name":"Biomechanics","url":"https://www.academia.edu/Documents/in/Biomechanics"},{"id":4122,"name":"Computer Animation","url":"https://www.academia.edu/Documents/in/Computer_Animation"},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis"},{"id":9112,"name":"Physical Activity","url":"https://www.academia.edu/Documents/in/Physical_Activity"},{"id":9136,"name":"Wireless Sensor Networks","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Networks"},{"id":18391,"name":"Mobile Communication","url":"https://www.academia.edu/Documents/in/Mobile_Communication"},{"id":96959,"name":"Back Pain","url":"https://www.academia.edu/Documents/in/Back_Pain"},{"id":102883,"name":"Real Time Systems","url":"https://www.academia.edu/Documents/in/Real_Time_Systems"},{"id":129388,"name":"Patient Monitoring","url":"https://www.academia.edu/Documents/in/Patient_Monitoring"},{"id":154850,"name":"Mobile Device","url":"https://www.academia.edu/Documents/in/Mobile_Device"},{"id":188095,"name":"Legged Locomotion","url":"https://www.academia.edu/Documents/in/Legged_Locomotion"},{"id":229390,"name":"Real Time","url":"https://www.academia.edu/Documents/in/Real_Time"},{"id":297691,"name":"High performance","url":"https://www.academia.edu/Documents/in/High_performance"},{"id":571336,"name":"Real Time Monitoring","url":"https://www.academia.edu/Documents/in/Real_Time_Monitoring"},{"id":741308,"name":"Wireless Sensing","url":"https://www.academia.edu/Documents/in/Wireless_Sensing"}],"urls":[{"id":1877757,"url":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5752156"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5011069"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5011069/Sentence_Level_and_Document_Level_Sentiment_Mining_for_Arabic_Texts"><img alt="Research paper thumbnail of Sentence-Level and Document-Level Sentiment Mining for Arabic Texts" class="work-thumbnail" src="https://attachments.academia-assets.com/34618848/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5011069/Sentence_Level_and_Document_Level_Sentiment_Mining_for_Arabic_Texts">Sentence-Level and Document-Level Sentiment Mining for Arabic Texts</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, we investigate sentiment mining of Arabic text at both the sentence level and the d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb6622038d4f2b6ce03c803f7ee7242d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34618848,"asset_id":5011069,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34618848/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5011069"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5011069"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5011069; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5011069]").text(description); $(".js-view-count[data-work-id=5011069]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5011069; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='5011069']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 5011069, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eb6622038d4f2b6ce03c803f7ee7242d" } } $('.js-work-strip[data-work-id=5011069]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":5011069,"title":"Sentence-Level and Document-Level Sentiment Mining for Arabic Texts","translated_title":"","metadata":{"abstract":"In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="877132" id="papers"><div class="js-work-strip profile--work_container" data-work-id="123437086"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123437086/RTP_LX_Can_LLMs_Evaluate_Toxicity_in_Multilingual_Scenarios"><img alt="Research paper thumbnail of RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?" class="work-thumbnail" src="https://attachments.academia-assets.com/117867625/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123437086/RTP_LX_Can_LLMs_Evaluate_Toxicity_in_Multilingual_Scenarios">RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Apr 22, 2024</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="84e19e981f22013693f448b6ac3a5ecb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117867625,"asset_id":123437086,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117867625/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123437086"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123437086"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123437086; 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The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report. 1 http://competitions.codalab.org/ competitions/20011 2 http://scholar.harvard.edu/malmasi/ olid 3 A total of 800 teams signed up to participate in the task, but only 115 teams ended up submitting results eventually.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of the 13th International Workshop on Semantic Evaluation","grobid_abstract_attachment_id":92211251},"translated_abstract":null,"internal_url":"https://www.academia.edu/88190331/SemEval_2019_Task_6_Identifying_and_Categorizing_Offensive_Language_in_Social_Media_OffensEval_","translated_internal_url":"","created_at":"2022-10-09T15:29:02.092-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":794499,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":92211251,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211251/thumbnails/1.jpg","file_name":"1903.pdf","download_url":"https://www.academia.edu/attachments/92211251/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"SemEval_2019_Task_6_Identifying_and_Cate.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211251/1903-libre.pdf?1665355131=\u0026response-content-disposition=attachment%3B+filename%3DSemEval_2019_Task_6_Identifying_and_Cate.pdf\u0026Expires=1732498848\u0026Signature=Yxm1BENjAVyoSDDyJ5uM4GI8fTbMzGPl3vdy74Ck-2cnUp6ZdP3iep~HavTFPQfMmlTBa2qom~fWWWZK38XE1AhTlWICBjo8ag4R-hKz5ist9d~Zlqo-L26I3bCKm96CU06e6AKnzwUbBSqf2BuBr7zgI1QupOWoHKa33YK73OOIWmjtPrQjePnphTesfV6JDoLSe1mK4bkCRP~nWliqaAh~VImF-pEreKkOHjyLVL8P2x5DW2YpVVh853CJ~2s2x~DIDre7oA77ya08K-5VTDPpoJXlhAwVZ5xD6AtXJhN9i2Y0aP21rbne7ZlpyfSxOqZoYR6-Eo6IeCY2~dUgVg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"SemEval_2019_Task_6_Identifying_and_Categorizing_Offensive_Language_in_Social_Media_OffensEval_","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":794499,"first_name":"Noura","middle_initials":null,"last_name":"Farra","page_name":"NouraFarra","domain_name":"columbia","created_at":"2011-09-27T22:02:49.992-07:00","display_name":"Noura Farra","url":"https://columbia.academia.edu/NouraFarra"},"attachments":[{"id":92211251,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211251/thumbnails/1.jpg","file_name":"1903.pdf","download_url":"https://www.academia.edu/attachments/92211251/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"SemEval_2019_Task_6_Identifying_and_Cate.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211251/1903-libre.pdf?1665355131=\u0026response-content-disposition=attachment%3B+filename%3DSemEval_2019_Task_6_Identifying_and_Cate.pdf\u0026Expires=1732498848\u0026Signature=Yxm1BENjAVyoSDDyJ5uM4GI8fTbMzGPl3vdy74Ck-2cnUp6ZdP3iep~HavTFPQfMmlTBa2qom~fWWWZK38XE1AhTlWICBjo8ag4R-hKz5ist9d~Zlqo-L26I3bCKm96CU06e6AKnzwUbBSqf2BuBr7zgI1QupOWoHKa33YK73OOIWmjtPrQjePnphTesfV6JDoLSe1mK4bkCRP~nWliqaAh~VImF-pEreKkOHjyLVL8P2x5DW2YpVVh853CJ~2s2x~DIDre7oA77ya08K-5VTDPpoJXlhAwVZ5xD6AtXJhN9i2Y0aP21rbne7ZlpyfSxOqZoYR6-Eo6IeCY2~dUgVg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":544669,"name":"Language Identification","url":"https://www.academia.edu/Documents/in/Language_Identification"},{"id":663814,"name":"Offensive Realism","url":"https://www.academia.edu/Documents/in/Offensive_Realism"}],"urls":[]}, dispatcherData: dispatcherData }); 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Unlike previous work that is focused on using only English as the source language and a small number of target languages, we use multiple source languages to learn a more robust sentiment transfer model for 16 languages from different language families. Our approaches explore the potential of using an annotation projection approach and a direct transfer approach using cross-lingual word representations and neural networks. Whereas most previous work relies on machine translation, we show that we can build cross-lingual sentiment analysis systems without machine translation or even high quality parallel data. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190329"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190329/Predicting_the_Type_and_Target_of_Offensive_Posts_in_Social_Media"><img alt="Research paper thumbnail of Predicting the Type and Target of Offensive Posts in Social Media" class="work-thumbnail" src="https://attachments.academia-assets.com/92211249/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190329/Predicting_the_Type_and_Target_of_Offensive_Posts_in_Social_Media">Predicting the Type and Target of Offensive Posts in Social Media</a></div><div class="wp-workCard_item"><span>Proceedings of the 2019 Conference of the North</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a4f65f8d0a5847f88e70379b669d9604" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211249,"asset_id":88190329,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211249/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190329"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190329"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190329; 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However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190328"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190328/SemEval_2017_Task_4_Sentiment_Analysis_in_Twitter"><img alt="Research paper thumbnail of SemEval-2017 Task 4: Sentiment Analysis in Twitter" class="work-thumbnail" src="https://attachments.academia-assets.com/92211250/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190328/SemEval_2017_Task_4_Sentiment_Analysis_in_Twitter">SemEval-2017 Task 4: Sentiment Analysis in Twitter</a></div><div class="wp-workCard_item"><span>Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6dfad8e0f3b05e71da6c270449a71851" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211250,"asset_id":88190328,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211250/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190328; 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SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a twopoint and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)","grobid_abstract_attachment_id":92211250},"translated_abstract":null,"internal_url":"https://www.academia.edu/88190328/SemEval_2017_Task_4_Sentiment_Analysis_in_Twitter","translated_internal_url":"","created_at":"2022-10-09T15:29:01.596-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":794499,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":92211250,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211250/thumbnails/1.jpg","file_name":"S17-2088.pdf","download_url":"https://www.academia.edu/attachments/92211250/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"SemEval_2017_Task_4_Sentiment_Analysis_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211250/S17-2088-libre.pdf?1665355131=\u0026response-content-disposition=attachment%3B+filename%3DSemEval_2017_Task_4_Sentiment_Analysis_i.pdf\u0026Expires=1732498849\u0026Signature=OeT~Tb8YT-BbB692duLpy38YqyXeEcyFOx5FB83jptMyRFLkQXEdtt1nDzy1YRfHM55Yy~vvymwUmFvHnHRVc9kOXEmwsQ3wNKhvz3FjoXZZR-FkNtAai8LGg3oYaVYr8TZsP8oOwCOsNKGjpxZj5TFl~xXJbTDO-os7VeMuUqJF1W-QNFzBGDjMkvkZdjmNOP45c4zEBY1xo4~UAFybZtrJVayAvkChS3FKtkILXT4cFxamAaaZf46axNaKVSIVUroWtFXntlA9lW8CWz9JxLKL3Rae3oBthRxV9tcpKxrm~gsazOBp7RiQo7DUeFlBV8UvGlHYG1vwHrPhs45hcg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"SemEval_2017_Task_4_Sentiment_Analysis_in_Twitter","translated_slug":"","page_count":17,"language":"en","content_type":"Work","owner":{"id":794499,"first_name":"Noura","middle_initials":null,"last_name":"Farra","page_name":"NouraFarra","domain_name":"columbia","created_at":"2011-09-27T22:02:49.992-07:00","display_name":"Noura Farra","url":"https://columbia.academia.edu/NouraFarra"},"attachments":[{"id":92211250,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211250/thumbnails/1.jpg","file_name":"S17-2088.pdf","download_url":"https://www.academia.edu/attachments/92211250/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"SemEval_2017_Task_4_Sentiment_Analysis_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211250/S17-2088-libre.pdf?1665355131=\u0026response-content-disposition=attachment%3B+filename%3DSemEval_2017_Task_4_Sentiment_Analysis_i.pdf\u0026Expires=1732498849\u0026Signature=OeT~Tb8YT-BbB692duLpy38YqyXeEcyFOx5FB83jptMyRFLkQXEdtt1nDzy1YRfHM55Yy~vvymwUmFvHnHRVc9kOXEmwsQ3wNKhvz3FjoXZZR-FkNtAai8LGg3oYaVYr8TZsP8oOwCOsNKGjpxZj5TFl~xXJbTDO-os7VeMuUqJF1W-QNFzBGDjMkvkZdjmNOP45c4zEBY1xo4~UAFybZtrJVayAvkChS3FKtkILXT4cFxamAaaZf46axNaKVSIVUroWtFXntlA9lW8CWz9JxLKL3Rae3oBthRxV9tcpKxrm~gsazOBp7RiQo7DUeFlBV8UvGlHYG1vwHrPhs45hcg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190327/SMARTies_Sentiment_Models_for_Arabic_Target_entities"><img alt="Research paper thumbnail of SMARTies: Sentiment Models for Arabic Target entities" class="work-thumbnail" src="https://attachments.academia-assets.com/92211252/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190327/SMARTies_Sentiment_Models_for_Arabic_Target_entities">SMARTies: Sentiment Models for Arabic Target entities</a></div><div class="wp-workCard_item"><span>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="85e8c71f4154e187c811ee9bc67bc134" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211252,"asset_id":88190327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211252/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190327; 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The first stage consists of identifying candidate targets \"entities\" in a given text. The second stage consists of identifying the opinion polarity (positive, negative, or neutral) expressed about a specific entity. We annotate a corpus of Arabic text using this method, selecting our data from online commentaries in different domains. Despite the complexity of the task, we find high agreement. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190325"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190325/The_Columbia_System_in_the_QALB_2014_Shared_Task_on_Arabic_Error_Correction"><img alt="Research paper thumbnail of The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction" class="work-thumbnail" src="https://attachments.academia-assets.com/92211247/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190325/The_Columbia_System_in_the_QALB_2014_Shared_Task_on_Arabic_Error_Correction">The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction</a></div><div class="wp-workCard_item"><span>Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3cb58e72e5df15f45efeec38e8b844b1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211247,"asset_id":88190325,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211247/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190325"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190325"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190325; 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Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2525ba2ad785c782e6de0fe3718a4ab0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211253,"asset_id":88190324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211253/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190324"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88190324]").text(description); $(".js-view-count[data-work-id=88190324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88190324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88190324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88190324, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2525ba2ad785c782e6de0fe3718a4ab0" } } $('.js-work-strip[data-work-id=88190324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88190324,"title":"Large Scale Arabic Error Annotation: Guidelines and Framework","translated_title":"","metadata":{"abstract":"We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations."},"translated_abstract":"We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.","internal_url":"https://www.academia.edu/88190324/Large_Scale_Arabic_Error_Annotation_Guidelines_and_Framework","translated_internal_url":"","created_at":"2022-10-09T15:29:01.025-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":794499,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":92211253,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211253/thumbnails/1.jpg","file_name":"ZAGHOUANI14.956.L14-1721.pdf","download_url":"https://www.academia.edu/attachments/92211253/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Large_Scale_Arabic_Error_Annotation_Guid.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211253/ZAGHOUANI14.956.L14-1721-libre.pdf?1665355125=\u0026response-content-disposition=attachment%3B+filename%3DLarge_Scale_Arabic_Error_Annotation_Guid.pdf\u0026Expires=1732498849\u0026Signature=Pc5XDmi2HsHzNDwDKCDRIa29tT72nbzMmU1fME7BZIvCjJm~HeqokUtIu5qyRUPpSSwCYh4XT8N41VsWh8QZKMraabeSltVKBZ24ej7rGIXtHF1n5KpEPVroc6G3z-i9rLlTAd40-aoWI8xUUE9SFejd3AhXX7-~qOMBu1v5T-~tlOglJVPqEUW90FZudqZ-faUj-6AWjAiZ1puVDNtDyQgvWUsumlYmHSY-ydp6-VBxiMXOFt0AlX7GofV2myF2aoiCeYW0Uzwt8-lrDb5RHdWg92LuVumcXyu8iIU08O~FyE7mVXMMyhaOeH6YdjE-nJoa-OgC5wnNtB~MYxq7pQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Large_Scale_Arabic_Error_Annotation_Guidelines_and_Framework","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":794499,"first_name":"Noura","middle_initials":null,"last_name":"Farra","page_name":"NouraFarra","domain_name":"columbia","created_at":"2011-09-27T22:02:49.992-07:00","display_name":"Noura Farra","url":"https://columbia.academia.edu/NouraFarra"},"attachments":[{"id":92211253,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92211253/thumbnails/1.jpg","file_name":"ZAGHOUANI14.956.L14-1721.pdf","download_url":"https://www.academia.edu/attachments/92211253/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Large_Scale_Arabic_Error_Annotation_Guid.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92211253/ZAGHOUANI14.956.L14-1721-libre.pdf?1665355125=\u0026response-content-disposition=attachment%3B+filename%3DLarge_Scale_Arabic_Error_Annotation_Guid.pdf\u0026Expires=1732498849\u0026Signature=Pc5XDmi2HsHzNDwDKCDRIa29tT72nbzMmU1fME7BZIvCjJm~HeqokUtIu5qyRUPpSSwCYh4XT8N41VsWh8QZKMraabeSltVKBZ24ej7rGIXtHF1n5KpEPVroc6G3z-i9rLlTAd40-aoWI8xUUE9SFejd3AhXX7-~qOMBu1v5T-~tlOglJVPqEUW90FZudqZ-faUj-6AWjAiZ1puVDNtDyQgvWUsumlYmHSY-ydp6-VBxiMXOFt0AlX7GofV2myF2aoiCeYW0Uzwt8-lrDb5RHdWg92LuVumcXyu8iIU08O~FyE7mVXMMyhaOeH6YdjE-nJoa-OgC5wnNtB~MYxq7pQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":38072,"name":"Annotation","url":"https://www.academia.edu/Documents/in/Annotation"},{"id":49267,"name":"Computational Linguistics \u0026 NLP","url":"https://www.academia.edu/Documents/in/Computational_Linguistics_and_NLP"},{"id":235465,"name":"Guidelines","url":"https://www.academia.edu/Documents/in/Guidelines"},{"id":630181,"name":"Error Annotation","url":"https://www.academia.edu/Documents/in/Error_Annotation"},{"id":3425866,"name":"Corpus Compilation","url":"https://www.academia.edu/Documents/in/Corpus_Compilation"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190323"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190323/Generalized_Character_Level_Spelling_Error_Correction"><img alt="Research paper thumbnail of Generalized Character-Level Spelling Error Correction" class="work-thumbnail" src="https://attachments.academia-assets.com/92211254/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190323/Generalized_Character_Level_Spelling_Error_Correction">Generalized Character-Level Spelling Error Correction</a></div><div class="wp-workCard_item"><span>Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cf692fa42044b275c8dc035a87686fe1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211254,"asset_id":88190323,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211254/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190323"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190323"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190323; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88190322"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88190322/A_Mobile_Sensing_and_Imaging_System_for_Real_Time_Monitoring_of_Spine_Health"><img alt="Research paper thumbnail of A Mobile Sensing and Imaging System for Real-Time Monitoring of Spine Health" class="work-thumbnail" src="https://attachments.academia-assets.com/92211245/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88190322/A_Mobile_Sensing_and_Imaging_System_for_Real_Time_Monitoring_of_Spine_Health">A Mobile Sensing and Imaging System for Real-Time Monitoring of Spine Health</a></div><div class="wp-workCard_item"><span>Journal of Medical Imaging and Health Informatics</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="411568d475c7bffe936eb9063f7756c8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92211245,"asset_id":88190322,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92211245/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88190322"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88190322"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88190322; 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For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. Spine stress can also worsen existing structural deformities in the spine such as adolescent idiopathic scoliosis (AIS). In this work we developed a system to monitor spine health through both dynamic monitoring and structural imaging. The dynamic sensing method monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing, and provides real-time user feedback when poor posture is sustained. The imaging method extracts the structural curvature of the spine and is used for the diagnosis of AIS in a non-invasive and inexpensive manner. Namely, the image is obtained using a photograph where the spinous processes have been marked to trace the shape of the spine. The spine curvature is then extracted automatically and modeled by a curve-fitting polynomial. The approach is simple and practical and allows scoliosis patients to monitor their curvature progress from home while minimizing the use of X-rays. The theme of our work is spine health, which we monitor through the wireless sensing system and the orthopedic imaging system. The two are complementary: the mobile wireless system assesses spine health during daily activity while the imaging system can assess the progression of a patient's structural spine curvature. We demonstrate effectiveness of our sensing system in simultaneously monitoring posture and position by testing in numerous situations. Furthermore, experiments show that our imaging method is accurate and robust under different brightness conditions. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="8186340"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/8186340/Generalized_Character_Level_Spelling_Error_Correction"><img alt="Research paper thumbnail of Generalized Character-Level Spelling Error Correction" class="work-thumbnail" src="https://attachments.academia-assets.com/34618825/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/8186340/Generalized_Character_Level_Spelling_Error_Correction">Generalized Character-Level Spelling Error Correction</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="26d22859b5446178d5e4d37f67179407" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34618825,"asset_id":8186340,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34618825/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="8186340"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="8186340"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8186340; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5011071"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5011071/A_novel_mobile_wireless_sensing_system_for_realtime_monitoring_of_posture_and_spine_stress"><img alt="Research paper thumbnail of A novel mobile wireless sensing system for realtime monitoring of posture and spine stress" class="work-thumbnail" src="https://attachments.academia-assets.com/34618863/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5011071/A_novel_mobile_wireless_sensing_system_for_realtime_monitoring_of_posture_and_spine_stress">A novel mobile wireless sensing system for realtime monitoring of posture and spine stress</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. We have developed a novel wireless mobile sensing system which monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing. The system provides a new method of measuring spine stress at both the back and the feet by integrating posture sensors with strain sensors. Posture and strain data is collected by means of a posture sensor at the neck and weight sensors at the feet. Data is transmitted wirelessly to a central processing station and real-time feedback is provided to the user's mobile device when sustained bad posture is detected. Moreover, the position of the patient (sitting, standing, or walking) can be determined by analysis of the weight sensor data and is visualized in real-time, along with back posture, at the central station by means of a graphical animation. Finally, data from all sensors is stored in a database to enable post processing and data analysis, and a summary report of daily posture and physical activity is sent to the user's email. The use of centralized processing allows for high performance data analysis and storage at the central station which enables tracking of the individual's progress. We demonstrate effectiveness of our system in simultaneously monitoring posture and position by testing in numerous situations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8ff7300afb45599012c91458e83ba61c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34618863,"asset_id":5011071,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34618863/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5011071"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5011071"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5011071; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5011071]").text(description); $(".js-view-count[data-work-id=5011071]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5011071; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='5011071']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 5011071, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8ff7300afb45599012c91458e83ba61c" } } $('.js-work-strip[data-work-id=5011071]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":5011071,"title":"A novel mobile wireless sensing system for realtime monitoring of posture and spine stress","translated_title":"","metadata":{"abstract":"Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. We have developed a novel wireless mobile sensing system which monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing. The system provides a new method of measuring spine stress at both the back and the feet by integrating posture sensors with strain sensors. Posture and strain data is collected by means of a posture sensor at the neck and weight sensors at the feet. Data is transmitted wirelessly to a central processing station and real-time feedback is provided to the user's mobile device when sustained bad posture is detected. Moreover, the position of the patient (sitting, standing, or walking) can be determined by analysis of the weight sensor data and is visualized in real-time, along with back posture, at the central station by means of a graphical animation. Finally, data from all sensors is stored in a database to enable post processing and data analysis, and a summary report of daily posture and physical activity is sent to the user's email. The use of centralized processing allows for high performance data analysis and storage at the central station which enables tracking of the individual's progress. We demonstrate effectiveness of our system in simultaneously monitoring posture and position by testing in numerous situations.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}}},"translated_abstract":"Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. We have developed a novel wireless mobile sensing system which monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing. The system provides a new method of measuring spine stress at both the back and the feet by integrating posture sensors with strain sensors. Posture and strain data is collected by means of a posture sensor at the neck and weight sensors at the feet. Data is transmitted wirelessly to a central processing station and real-time feedback is provided to the user's mobile device when sustained bad posture is detected. Moreover, the position of the patient (sitting, standing, or walking) can be determined by analysis of the weight sensor data and is visualized in real-time, along with back posture, at the central station by means of a graphical animation. Finally, data from all sensors is stored in a database to enable post processing and data analysis, and a summary report of daily posture and physical activity is sent to the user's email. The use of centralized processing allows for high performance data analysis and storage at the central station which enables tracking of the individual's progress. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5011069"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5011069/Sentence_Level_and_Document_Level_Sentiment_Mining_for_Arabic_Texts"><img alt="Research paper thumbnail of Sentence-Level and Document-Level Sentiment Mining for Arabic Texts" class="work-thumbnail" src="https://attachments.academia-assets.com/34618848/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5011069/Sentence_Level_and_Document_Level_Sentiment_Mining_for_Arabic_Texts">Sentence-Level and Document-Level Sentiment Mining for Arabic Texts</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, we investigate sentiment mining of Arabic text at both the sentence level and the d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb6622038d4f2b6ce03c803f7ee7242d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34618848,"asset_id":5011069,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34618848/download_file?st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTI0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5011069"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5011069"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5011069; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5011069]").text(description); $(".js-view-count[data-work-id=5011069]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5011069; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='5011069']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 5011069, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eb6622038d4f2b6ce03c803f7ee7242d" } } $('.js-work-strip[data-work-id=5011069]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":5011069,"title":"Sentence-Level and Document-Level Sentiment Mining for Arabic Texts","translated_title":"","metadata":{"abstract":"In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. 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