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In recent years, breakthroughs achieved using end-to-end neural network" /> <title>(PDF) Korean Semantic Role Labeling with Bidirectional Encoder Representations from Transformers and Simple Semantic Information | Changki Lee - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/82771763/Korean_Semantic_Role_Labeling_with_Bidirectional_Encoder_Representations_from_Transformers_and_Simple_Semantic_Information" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '9387f500ddcbb8d05c67bef28a2fe0334f1aafb8'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1733035034000); window.Aedu.timeDifference = new Date().getTime() - 1733035034000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"State-of-the-art semantic role labeling (SRL) performance has been achieved using neural network models by incorporating syntactic feature information such as dependency trees. In recent years, breakthroughs achieved using end-to-end neural network models have resulted in a state-of-the-art SRL performance even without syntactic features. With the advent of a language model called bidirectional encoder representations from transformers (BERT), another breakthrough was witnessed. Even though the semantic information of each word constituting a sentence is important in determining the meaning of a word, previous studies regarding the end-to-end neural network method did not utilize semantic information. In this study, we propose a BERT-based SRL model that uses simple semantic information without syntactic feature information. To obtain the latter, we used PropBank, which described the relational information between predicates and arguments. In addition, text-originated feature inform...","author":[{"@context":"https://schema.org","@type":"Person","name":"Changki Lee"}],"contributor":[],"dateCreated":"2022-07-07","dateModified":"2022-07-07","datePublished":null,"headline":"Korean Semantic Role Labeling with Bidirectional Encoder Representations from Transformers and Simple Semantic Information","inLanguage":"en","keywords":["Applied Sciences"],"locationCreated":null,"publication":"Applied Sciences","publisher":{"@context":"https://schema.org","@type":"Organization","name":"MDPI AG"},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/82771763/Korean_Semantic_Role_Labeling_with_Bidirectional_Encoder_Representations_from_Transformers_and_Simple_Semantic_Information","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"kangwon"}]}</script><link rel="stylesheet" media="all" 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{"work":{"id":82771763,"created_at":"2022-07-07T21:30:32.988-07:00","from_world_paper_id":210048379,"updated_at":"2022-07-08T20:26:33.224-07:00","_data":{"abstract":"State-of-the-art semantic role labeling (SRL) performance has been achieved using neural network models by incorporating syntactic feature information such as dependency trees. In recent years, breakthroughs achieved using end-to-end neural network models have resulted in a state-of-the-art SRL performance even without syntactic features. With the advent of a language model called bidirectional encoder representations from transformers (BERT), another breakthrough was witnessed. Even though the semantic information of each word constituting a sentence is important in determining the meaning of a word, previous studies regarding the end-to-end neural network method did not utilize semantic information. In this study, we propose a BERT-based SRL model that uses simple semantic information without syntactic feature information. To obtain the latter, we used PropBank, which described the relational information between predicates and arguments. In addition, text-originated feature inform...","publisher":"MDPI AG","publication_name":"Applied Sciences"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Korean Semantic Role Labeling with Bidirectional Encoder Representations from Transformers and Simple Semantic Information","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [34012167]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":88368087,"attachmentType":"pdf"}"><img alt="First page of “Korean Semantic Role Labeling with Bidirectional Encoder Representations from Transformers and Simple Semantic Information”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/88368087/mini_magick20220708-8422-1xkr0tn.png?1657338569" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Korean Semantic Role Labeling with Bidirectional Encoder Representations from Transformers and Simple Semantic Information</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="34012167" href="https://kangwon.academia.edu/ChangkiLee"><img alt="Profile image of Changki Lee" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Changki Lee</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">Applied Sciences</p></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">State-of-the-art semantic role labeling (SRL) performance has been achieved using neural network models by incorporating syntactic feature information such as dependency trees. In recent years, breakthroughs achieved using end-to-end neural network models have resulted in a state-of-the-art SRL performance even without syntactic features. With the advent of a language model called bidirectional encoder representations from transformers (BERT), another breakthrough was witnessed. Even though the semantic information of each word constituting a sentence is important in determining the meaning of a word, previous studies regarding the end-to-end neural network method did not utilize semantic information. In this study, we propose a BERT-based SRL model that uses simple semantic information without syntactic feature information. To obtain the latter, we used PropBank, which described the relational information between predicates and arguments. In addition, text-originated feature inform...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":88368087,"attachmentType":"pdf","workUrl":"https://www.academia.edu/82771763/Korean_Semantic_Role_Labeling_with_Bidirectional_Encoder_Representations_from_Transformers_and_Simple_Semantic_Information"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":88368087,"attachmentType":"pdf","workUrl":"https://www.academia.edu/82771763/Korean_Semantic_Role_Labeling_with_Bidirectional_Encoder_Representations_from_Transformers_and_Simple_Semantic_Information"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" 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class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/57155604/The_effect_of_syntactic_representation_on_semantic_role_labeling"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="13506130" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/13506130/Calibrating_features_for_semantic_role_labeling">Calibrating features for semantic role labeling</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32707061" href="https://hku-hk.academia.edu/jiawenliu">jiawen liu</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2004</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Calibrating features for semantic role labeling","attachmentId":45259595,"attachmentType":"pdf","work_url":"https://www.academia.edu/13506130/Calibrating_features_for_semantic_role_labeling","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/13506130/Calibrating_features_for_semantic_role_labeling"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="38661986" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/38661986/Syntax_aware_Neural_Semantic_Role_Labeling">Syntax-aware Neural Semantic Role Labeling</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="106967921" href="https://bit.academia.edu/YaWang">Ya Wang</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic structures, traditional discrete-feature-based SRL approaches make heavy use of syntactic features. In contrast, deep-neural-network-based approaches usually encode the input sentence as a word sequence without considering the syntactic structures. In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are beneficial for neural SRL models. Experiments on the benchmark CoNLL-2005 dataset show that syntax-aware SRL approaches can effectively improve performance over a strong baseline with external word representations from ELMo. With the extra syntax-aware representations, our approaches achieve new state-of-the-art 85.6 F1 (single model) and 86.6 F1 (ensemble) on the test data, outperforming the corresponding strong baselines with ELMo by 0.8 and 1.0, respectively. Detailed error analysis are conducted to gain more insights on the investigated approaches.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Syntax-aware Neural Semantic Role Labeling","attachmentId":58741556,"attachmentType":"pdf","work_url":"https://www.academia.edu/38661986/Syntax_aware_Neural_Semantic_Role_Labeling","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/38661986/Syntax_aware_Neural_Semantic_Role_Labeling"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="49886719" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/49886719/Using_a_Hybrid_Convolution_Tree_Kernel_for_Semantic_Role_Labeling">Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="116033475" href="https://independent.academia.edu/ChewLimTan">Chew Lim Tan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">ACM Transactions on Asian Language Information Processing, 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">As a kind of Shallow Semantic Parsing, Semantic Role Labeling (SRL) is gaining more attention as it benefits a wide range of natural language processing applications. Given a sentence, the task of SRL is to recognize semantic arguments (roles) for each predicate (target verb or noun). Feature-based methods have achieved much success in SRL and are regarded as the state-of-the-art methods for SRL. However, these methods are less effective in modeling structured features. As an extension of feature-based methods, kernel-based methods are able to capture structured features more efficiently in a much higher dimension. Application of kernel methods to SRL has been achieved by selecting the tree portion of a predicate and one of its arguments as feature space, which is named as predicate-argument feature (PAF) kernel. The PAF kernel captures the syntactic tree structure features using convolution tree kernel, however, it does not distinguish between the path structure and the constituent...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling","attachmentId":68077460,"attachmentType":"pdf","work_url":"https://www.academia.edu/49886719/Using_a_Hybrid_Convolution_Tree_Kernel_for_Semantic_Role_Labeling","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/49886719/Using_a_Hybrid_Convolution_Tree_Kernel_for_Semantic_Role_Labeling"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":88368087,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":88368087,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_88368087" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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data-author-id="107703397" href="https://independent.academia.edu/JunichiTsujii">Junichi Tsujii</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Natural Language Processing, 2014</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Generalization of Semantic Roles in Automatic Semantic Role Labeling","attachmentId":74884755,"attachmentType":"pdf","work_url":"https://www.academia.edu/62000768/Generalization_of_Semantic_Roles_in_Automatic_Semantic_Role_Labeling","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/62000768/Generalization_of_Semantic_Roles_in_Automatic_Semantic_Role_Labeling"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="8" data-entity-id="60939528" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/60939528/DeSRL_A_Linear_Time_Semantic_Role_Labeling_System">DeSRL: A Linear-Time Semantic Role Labeling System</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37982119" href="https://independent.academia.edu/GiuseppeAttardi">Giuseppe Attardi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the Twelfth Conference on Computational Natural Language Learning, 2008</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"DeSRL: A Linear-Time Semantic Role Labeling System","attachmentId":74164212,"attachmentType":"pdf","work_url":"https://www.academia.edu/60939528/DeSRL_A_Linear_Time_Semantic_Role_Labeling_System","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/60939528/DeSRL_A_Linear_Time_Semantic_Role_Labeling_System"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div 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