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language processing (NLP)</title> <meta name="description" content="Search results for: natural language processing (NLP)"> <meta name="keywords" content="natural language processing (NLP)"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="natural language processing (NLP)"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 12137</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: natural language processing (NLP)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12077</span> Learner's Difficulties Acquiring English: The Case of Native Speakers of Rio de La Plata Spanish Towards Justifying the Need for Corpora</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Zinnia%20Bardas%20Hoffmann">Maria Zinnia Bardas Hoffmann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Contrastive Analysis (CA) is the systematic comparison between two languages. It stems from the notion that errors are caused by interference of the L1 system in the acquisition process of an L2. CA represents a useful tool to understand the nature of learning and acquisition. Also, this particular method promises a path to un-derstand the nature of underlying cognitive processes, even when other factors such as intrinsic motivation and teaching strategies were found to best explain student’s problems in acquisition. CA study is justified not only from the need to get a deeper understanding of the nature of SLA, but as an invaluable source to provide clues, at a cognitive level, for those general processes involved in rule formation and abstract thought. It is relevant for cross disciplinary studies and the fields of Computational Thought, Natural Language processing, Applied Linguistics, Cognitive Linguistics and Math Theory. That being said, this paper intends to address here as well its own set of constraints and limitations. Finally, this paper: (a) aims at identifying some of the difficulties students may find in their learning process due to the nature of their specific variety of L1, Rio de la Plata Spanish (RPS), (b) represents an attempt to discuss the necessity for specific models to approach CA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=second%20language%20acquisition" title="second language acquisition">second language acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=applied%20linguistics" title=" applied linguistics"> applied linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=contrastive%20analysis" title=" contrastive analysis"> contrastive analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=applied%20contrastive%20analysis%20English%20language%20department" title=" applied contrastive analysis English language department"> applied contrastive analysis English language department</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-linguistic%20rules" title=" meta-linguistic rules"> meta-linguistic rules</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-linguistics%20studies" title=" cross-linguistics studies"> cross-linguistics studies</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20thought" title=" computational thought"> computational thought</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/113980/learners-difficulties-acquiring-english-the-case-of-native-speakers-of-rio-de-la-plata-spanish-towards-justifying-the-need-for-corpora" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113980.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12076</span> An Event-Related Potentials Study on the Processing of English Subjunctive Mood by Chinese ESL Learners</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Huang">Yan Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Event-related potentials (ERPs) technique helps researchers to make continuous measures on the whole process of language comprehension, with an excellent temporal resolution at the level of milliseconds. The research on sentence processing has developed from the behavioral level to the neuropsychological level, which brings about a variety of sentence processing theories and models. However, the applicability of these models to L2 learners is still under debate. Therefore, the present study aims to investigate the neural mechanisms underlying English subjunctive mood processing by Chinese ESL learners. To this end, English subject clauses with subjunctive moods are used as the stimuli, all of which follow the same syntactic structure, “It is + adjective + that … + (should) do + …” Besides, in order to examine the role that language proficiency plays on L2 processing, this research deals with two groups of Chinese ESL learners (18 males and 22 females, mean age=21.68), namely, high proficiency group (Group H) and low proficiency group (Group L). Finally, the behavioral and neurophysiological data analysis reveals the following findings: 1) Syntax and semantics interact with each other on the SECOND phase (300-500ms) of sentence processing, which is partially in line with the Three-phase Sentence Model; 2) Language proficiency does affect L2 processing. Specifically, for Group H, it is the syntactic processing that plays the dominant role in sentence processing while for Group L, semantic processing also affects the syntactic parsing during the THIRD phase of sentence processing (500-700ms). Besides, Group H, compared to Group L, demonstrates a richer native-like ERPs pattern, which further demonstrates the role of language proficiency in L2 processing. Based on the research findings, this paper also provides some enlightenment for the L2 pedagogy as well as the L2 proficiency assessment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20ESL%20learners" title="Chinese ESL learners">Chinese ESL learners</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20subjunctive%20mood" title=" English subjunctive mood"> English subjunctive mood</a>, <a href="https://publications.waset.org/abstracts/search?q=ERPs" title=" ERPs"> ERPs</a>, <a href="https://publications.waset.org/abstracts/search?q=L2%20processing" title=" L2 processing"> L2 processing</a> </p> <a href="https://publications.waset.org/abstracts/105431/an-event-related-potentials-study-on-the-processing-of-english-subjunctive-mood-by-chinese-esl-learners" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105431.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12075</span> Unsupervised Part-of-Speech Tagging for Amharic Using K-Means Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zelalem%20Fantahun">Zelalem Fantahun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Part-of-speech tagging is the process of assigning a part-of-speech or other lexical class marker to each word into naturally occurring text. Part-of-speech tagging is the most fundamental and basic task almost in all natural language processing. In natural language processing, the problem of providing large amount of manually annotated data is a knowledge acquisition bottleneck. Since, Amharic is one of under-resourced language, the availability of tagged corpus is the bottleneck problem for natural language processing especially for POS tagging. A promising direction to tackle this problem is to provide a system that does not require manually tagged data. In unsupervised learning, the learner is not provided with classifications. Unsupervised algorithms seek out similarity between pieces of data in order to determine whether they can be characterized as forming a group. This paper explicates the development of unsupervised part-of-speech tagger using K-Means clustering for Amharic language since large amount of data is produced in day-to-day activities. In the development of the tagger, the following procedures are followed. First, the unlabeled data (raw text) is divided into 10 folds and tokenization phase takes place; at this level, the raw text is chunked at sentence level and then into words. The second phase is feature extraction which includes word frequency, syntactic and morphological features of a word. The third phase is clustering. Among different clustering algorithms, K-means is selected and implemented in this study that brings group of similar words together. The fourth phase is mapping, which deals with looking at each cluster carefully and the most common tag is assigned to a group. This study finds out two features that are capable of distinguishing one part-of-speech from others these are morphological feature and positional information and show that it is possible to use unsupervised learning for Amharic POS tagging. In order to increase performance of the unsupervised part-of-speech tagger, there is a need to incorporate other features that are not included in this study, such as semantic related information. Finally, based on experimental result, the performance of the system achieves a maximum of 81% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=POS%20tagging" title="POS tagging">POS tagging</a>, <a href="https://publications.waset.org/abstracts/search?q=Amharic" title=" Amharic"> Amharic</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a> </p> <a href="https://publications.waset.org/abstracts/84660/unsupervised-part-of-speech-tagging-for-amharic-using-k-means-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84660.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12074</span> The Impact of Recurring Events in Fake News Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Raza">Ali Raza</a>, <a href="https://publications.waset.org/abstracts/search?q=Shafiq%20Ur%20Rehman%20Khan"> Shafiq Ur Rehman Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Raja%20Sher%20Afgun%20Usmani"> Raja Sher Afgun Usmani</a>, <a href="https://publications.waset.org/abstracts/search?q=Asif%20Raza"> Asif Raza</a>, <a href="https://publications.waset.org/abstracts/search?q=Basit%20Umair"> Basit Umair</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection of Fake news and missing information is gaining popularity, especially after the advancement in social media and online news platforms. Social media platforms are the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to identify whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use two models BERT and Bi-LSTM to investigate, and it is concluded from BERT we get better results and 70% of true news are recurring and rest of 30% are non-recurring. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=fake%20news%20detection" title=" fake news detection"> fake news detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title=" Bi-LSTM"> Bi-LSTM</a> </p> <a href="https://publications.waset.org/abstracts/190551/the-impact-of-recurring-events-in-fake-news-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190551.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">24</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12073</span> Leveraging Sentiment Analysis for Quality Improvement in Digital Healthcare Services</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naman%20Jain">Naman Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaun%20Fernandes"> Shaun Fernandes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing prevalence of online healthcare services, selecting the most suitable doctor has become a complex task, requiring careful consideration of both public sentiment and personal preferences. This paper proposes a sentiment analysis-driven method that integrates public reviews with user-specific criteria and correlated attributes to recommend online doctors. By leveraging Natural Language Processing (NLP) techniques, public sentiment is extracted from online reviews, which is then combined with user-defined preferences such as specialty, years of experience, location, and consultation fees. Additionally, correlated attributes like education and certifications are incorporated to enhance the recommendation accuracy. Experimental results demonstrate that the proposed system significantly improves user satisfaction by providing personalized doctor recommendations that align with both public opinion and individual needs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20doctors" title=" online doctors"> online doctors</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20preferences" title=" personal preferences"> personal preferences</a>, <a href="https://publications.waset.org/abstracts/search?q=correlated%20attributes" title=" correlated attributes"> correlated attributes</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/194882/leveraging-sentiment-analysis-for-quality-improvement-in-digital-healthcare-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194882.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">11</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12072</span> Native Language Identification with Cross-Corpus Evaluation Using Social Media Data: ’Reddit’</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasmeen%20Bassas">Yasmeen Bassas</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Kuebler"> Sandra Kuebler</a>, <a href="https://publications.waset.org/abstracts/search?q=Allen%20Riddell"> Allen Riddell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Native language identification is one of the growing subfields in natural language processing (NLP). The task of native language identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features, when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL), and then the trained models are evaluated on different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and logistic regression. Results show that content-based features are more accurate and robust than content independent ones when tested within the corpus and across corpus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLI" title="NLI">NLI</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=content-based%20features" title=" content-based features"> content-based features</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20independent%20features" title=" content independent features"> content independent features</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media%20corpus" title=" social media corpus"> social media corpus</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a> </p> <a href="https://publications.waset.org/abstracts/142396/native-language-identification-with-cross-corpus-evaluation-using-social-media-data-reddit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142396.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12071</span> “Presently”: A Personal Trainer App to Self-Train and Improve Presentation Skills</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shyam%20Mehraaj">Shyam Mehraaj</a>, <a href="https://publications.waset.org/abstracts/search?q=Samanthi%20E.%20R.%20Siriwardana"> Samanthi E. R. Siriwardana</a>, <a href="https://publications.waset.org/abstracts/search?q=Shehara%20A.%20K.%20G.%20H."> Shehara A. K. G. H.</a>, <a href="https://publications.waset.org/abstracts/search?q=Wanigasinghe%20N.%20T."> Wanigasinghe N. T.</a>, <a href="https://publications.waset.org/abstracts/search?q=Wandana%20R.%20A.%20K."> Wandana R. A. K.</a>, <a href="https://publications.waset.org/abstracts/search?q=Wedage%20C.%20V."> Wedage C. V.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A presentation is a critical tool for conveying not just spoken information but also a wide spectrum of human emotions. The single most effective thing to make the presentation successful is to practice it beforehand. Preparing for a presentation has been shown to be essential for improving emotional control, intonation and prosody, pronunciation, and vocabulary, as well as the quality of the presentation slides. As a result, practicing has become one of the most critical parts of giving a good presentation. In this research, the main focus is to analyze the audio, video, and slides of the presentation uploaded by the presenters. This proposed solution is based on the Natural Language Processing and Computer Vision techniques to cater to the requirement for the presenter to do a presentation beforehand using a mobile responsive web application. The proposed system will assist in practicing the presentation beforehand by identifying the presenters’ emotions, body language, tonality, prosody, pronunciations and vocabulary, and presentation slides quality. Overall, the system will give a rating and feedback to the presenter about the performance so that the presenters’ can improve their presentation skills. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=presentation" title="presentation">presentation</a>, <a href="https://publications.waset.org/abstracts/search?q=self-evaluation" title=" self-evaluation"> self-evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20learning%20processing" title=" natural learning processing"> natural learning processing</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/150559/presently-a-personal-trainer-app-to-self-train-and-improve-presentation-skills" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150559.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12070</span> Twitter Sentiment Analysis during the Lockdown on New-Zealand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smah%20Almotiri">Smah Almotiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter%20analysis" title=" Twitter analysis"> Twitter analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=lockdown" title=" lockdown"> lockdown</a>, <a href="https://publications.waset.org/abstracts/search?q=Covid-19" title=" Covid-19"> Covid-19</a>, <a href="https://publications.waset.org/abstracts/search?q=AFINN" title=" AFINN"> AFINN</a>, <a href="https://publications.waset.org/abstracts/search?q=NodeJS" title=" NodeJS"> NodeJS</a> </p> <a href="https://publications.waset.org/abstracts/143752/twitter-sentiment-analysis-during-the-lockdown-on-new-zealand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143752.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12069</span> Methodology for Developing an Intelligent Tutoring System Based on Marzano’s Taxonomy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joaquin%20Navarro%20Perales">Joaquin Navarro Perales</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Lidia%20Franzoni%20Vel%C3%A1zquez"> Ana Lidia Franzoni Velázquez</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20Cervantes%20P%C3%A9rez"> Francisco Cervantes Pérez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Mexican educational system faces diverse challenges related with the quality and coverage of education. The development of Intelligent Tutoring Systems (ITS) may help to solve some of them by helping teachers to customize their classes according to the performance of the students in online courses. In this work, we propose the adaptation of a functional ITS based on Bloom’s taxonomy called <em>Sistema de Apoyo Generalizado para la Enseñanza Individualizada</em> (SAGE), to measure student’s metacognition and their emotional response based on Marzano’s taxonomy. The students and the system will share the control over the advance in the course, so they can improve their metacognitive skills. The system will not allow students to get access to subjects not mastered yet. The interaction between the system and the student will be implemented through Natural Language Processing techniques, thus avoiding the use of sensors to evaluate student’s response. The teacher will evaluate student’s knowledge utilization, which is equivalent to the last cognitive level in Marzano’s taxonomy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20tutoring%20systems" title="intelligent tutoring systems">intelligent tutoring systems</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20modelling" title=" student modelling"> student modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=metacognition" title=" metacognition"> metacognition</a>, <a href="https://publications.waset.org/abstracts/search?q=affective%20computing" title=" affective computing"> affective computing</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/96468/methodology-for-developing-an-intelligent-tutoring-system-based-on-marzanos-taxonomy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96468.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">197</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12068</span> Language Processing of Seniors with Alzheimer’s Disease: From the Perspective of Temporal Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lai%20Yi-Hsiu">Lai Yi-Hsiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present paper aims to examine the language processing of Chinese-speaking seniors with Alzheimer’s disease (AD) from the perspective of temporal cues. Twenty healthy adults, 17 healthy seniors, and 13 seniors with AD in Taiwan participated in this study to tell stories based on two sets of pictures. Nine temporal cues were fetched and analyzed. Oral productions in Mandarin Chinese were compared and discussed to examine to what extent and in what way these three groups of participants performed with significant differences. Results indicated that the age effects were significant in filled pauses. The dementia effects were significant in mean duration of pauses, empty pauses, filled pauses, lexical pauses, normalized mean duration of filled pauses and lexical pauses. The findings reported in the current paper help characterize the nature of language processing in seniors with or without AD, and contribute to the interactions between the AD neural mechanism and their temporal parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20processing" title="language processing">language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%E2%80%99s%20disease" title=" Alzheimer’s disease"> Alzheimer’s disease</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandarin%20Chinese" title=" Mandarin Chinese"> Mandarin Chinese</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20cues" title=" temporal cues"> temporal cues</a> </p> <a href="https://publications.waset.org/abstracts/62548/language-processing-of-seniors-with-alzheimers-disease-from-the-perspective-of-temporal-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62548.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">446</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12067</span> Probing Syntax Information in Word Representations with Deep Metric Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bowen%20Ding">Bowen Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Yihao%20Kuang"> Yihao Kuang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20metric%20learning" title="deep metric learning">deep metric learning</a>, <a href="https://publications.waset.org/abstracts/search?q=syntax%20tree%20probing" title=" syntax tree probing"> syntax tree probing</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20representations" title=" word representations"> word representations</a> </p> <a href="https://publications.waset.org/abstracts/173855/probing-syntax-information-in-word-representations-with-deep-metric-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173855.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">68</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12066</span> Multilingualism without a Dominant Language in the Preschool Age: A Case of Natural Italian-Russian-German-English Multilingualism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Legkikh%20Victoria">Legkikh Victoria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of keeping bi/multilingualism is usually a way to let the child speak two/three languages at the same level. The main problem which normally appears is a mixed language or a domination of one language. The same level of two or more languages would be ideal but practically not easily reachable. So it was made an experiment with a girl with a natural multilingualism as an attempt to avoid a dominant language in the preschool age. The girl lives in Germany and the main languages for her are Italian, Russian and German but she also hears every day English. ‘One parent – one language’ strategy was used since the beginning so Italian and Russian were spoken to her since her birth, English was spoken between the parents and when she was 1,5 it was added German as a language of a nursery. In order to avoid a dominant language, she was always put in international groups with activity in different languages. Even if it was not possible to avoid an interference of languages in this case we can talk not only about natural multilingualism but also about balanced bilingualism in preschool time. The languages have been developing in parallel with different accents in a different period. Now at the age of 6 we can see natural horizontal multilingualism Russian/Italian/German/English. At the moment, her Russian/Italian bilingualism is balanced. German vocabulary is less but the language is active and English is receptive. We can also see a reciprocal interference of all the three languages (English is receptive so the simple phrases are normally said correctly but they are not enough to judge the level of language interference and it is not noticed any ‘English’ mistakes in other languages). After analysis of the state of every language, we can see as a positive and negative result of the experiment. As a positive result we can see that in the age of 6 the girl does not refuse any language, three languages are active, she differentiate languages and even if she says a word from another language she notifies that it is not a correct word, and the most important are the fact, that she does not have a preferred language. As a prove of the last statement it is to be noticed not only her self-identification as ‘half Russian and half Italian’ but also an answer to the question about her ‘mother tongue’: ‘I do not know, probably, when I have my own children I will speak one day Russian and one day Italian and sometimes German’. As a negative result, we can notice that not only a development of all the three languages are a little bit slower than it is supposed for her age but since she does not have a dominating language she also does not have a ‘perfect’ language and the interference is reciprocal. In any case, the experiment shows that it is possible to keep at least two languages without a preference in a pre-school multilingual space. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=balanced%20bilingualism" title="balanced bilingualism">balanced bilingualism</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20interference" title=" language interference"> language interference</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20multilingualism" title=" natural multilingualism"> natural multilingualism</a>, <a href="https://publications.waset.org/abstracts/search?q=preschool%20multilingual%20education" title=" preschool multilingual education"> preschool multilingual education</a> </p> <a href="https://publications.waset.org/abstracts/56962/multilingualism-without-a-dominant-language-in-the-preschool-age-a-case-of-natural-italian-russian-german-english-multilingualism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56962.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">273</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12065</span> ViraPart: A Text Refinement Framework for Automatic Speech Recognition and Natural Language Processing Tasks in Persian</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Narges%20Farokhshad">Narges Farokhshad</a>, <a href="https://publications.waset.org/abstracts/search?q=Milad%20Molazadeh"> Milad Molazadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Saman%20Jamalabbasi"> Saman Jamalabbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Babaei%20Giglou"> Hamed Babaei Giglou</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Bibak"> Saeed Bibak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Persian language is an inflectional subject-object-verb language. This fact makes Persian a more uncertain language. However, using techniques such as Zero-Width Non-Joiner (ZWNJ) recognition, punctuation restoration, and Persian Ezafe construction will lead us to a more understandable and precise language. In most of the works in Persian, these techniques are addressed individually. Despite that, we believe that for text refinement in Persian, all of these tasks are necessary. In this work, we proposed a ViraPart framework that uses embedded ParsBERT in its core for text clarifications. First, used the BERT variant for Persian followed by a classifier layer for classification procedures. Next, we combined models outputs to output cleartext. In the end, the proposed model for ZWNJ recognition, punctuation restoration, and Persian Ezafe construction performs the averaged F1 macro scores of 96.90%, 92.13%, and 98.50%, respectively. Experimental results show that our proposed approach is very effective in text refinement for the Persian language. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Persian%20Ezafe" title="Persian Ezafe">Persian Ezafe</a>, <a href="https://publications.waset.org/abstracts/search?q=punctuation" title=" punctuation"> punctuation</a>, <a href="https://publications.waset.org/abstracts/search?q=ZWNJ" title=" ZWNJ"> ZWNJ</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=ParsBERT" title=" ParsBERT"> ParsBERT</a>, <a href="https://publications.waset.org/abstracts/search?q=transformers" title=" transformers"> transformers</a> </p> <a href="https://publications.waset.org/abstracts/143102/virapart-a-text-refinement-framework-for-automatic-speech-recognition-and-natural-language-processing-tasks-in-persian" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143102.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12064</span> Using Bidirectional Encoder Representations from Transformers to Extract Topic-Independent Sentiment Features for Social Media Bot Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Heidari">Maryam Heidari</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20H.%20Jones%20Jr."> James H. Jones Jr.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bot%20detection" title="bot detection">bot detection</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a> </p> <a href="https://publications.waset.org/abstracts/129049/using-bidirectional-encoder-representations-from-transformers-to-extract-topic-independent-sentiment-features-for-social-media-bot-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129049.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">116</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12063</span> DocPro: A Framework for Processing Semantic and Layout Information in Business Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming-Jen%20Huang">Ming-Jen Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Fang%20Huang"> Chun-Fang Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiching%20Wei"> Chiching Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20processing" title="document processing">document processing</a>, <a href="https://publications.waset.org/abstracts/search?q=framework" title=" framework"> framework</a>, <a href="https://publications.waset.org/abstracts/search?q=formal%20definition" title=" formal definition"> formal definition</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/126703/docpro-a-framework-for-processing-semantic-and-layout-information-in-business-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126703.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12062</span> Document-level Sentiment Analysis: An Exploratory Case Study of Low-resource Language Urdu</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ammarah%20Irum">Ammarah Irum</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ali%20Tahir"> Muhammad Ali Tahir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urdu%20sentiment%20analysis" title="urdu sentiment analysis">urdu sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title=" opinion mining"> opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=low-resource%20language" title=" low-resource language"> low-resource language</a> </p> <a href="https://publications.waset.org/abstracts/172973/document-level-sentiment-analysis-an-exploratory-case-study-of-low-resource-language-urdu" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172973.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12061</span> Literacy in First and Second Language: Implication for Language Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Inuwa%20Danladi%20Bawa">Inuwa Danladi Bawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the challenges of African states in the development of education in the past and the present is the problem of literacy. Literacy in the first language is seen as a strong base for the development of second language; they are mostly the language of education. Language development is an offshoot of language planning; so the need to develop literacy in both first and second language affects language education and predicts the extent of achievement of the entire education sector. The need to balance literacy acquisition in first language for good conditioning the acquisition of second language is paramount. Likely constraints that includes; non-standardization, underdeveloped and undeveloped first languages are among many. Solutions to some of these include the development of materials and use of the stages and levels of literacy acquisition. This is with believed that a child writes well in second language if he has literacy in the first language. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=first%20language" title="first language">first language</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20language" title=" second language"> second language</a>, <a href="https://publications.waset.org/abstracts/search?q=literacy" title=" literacy"> literacy</a>, <a href="https://publications.waset.org/abstracts/search?q=english%20language" title=" english language"> english language</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistics" title=" linguistics"> linguistics</a> </p> <a href="https://publications.waset.org/abstracts/3745/literacy-in-first-and-second-language-implication-for-language-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12060</span> Evaluation and Compression of Different Language Transformer Models for Semantic Textual Similarity Binary Task Using Minority Language Resources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ma.%20Gracia%20Corazon%20Cayanan">Ma. Gracia Corazon Cayanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Yuen%20Cheong"> Kai Yuen Cheong</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Sha"> Li Sha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20matching" title="semantic matching">semantic matching</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20textual%20similarity%20binary%20task" title=" semantic textual similarity binary task"> semantic textual similarity binary task</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20resource%20minority%20language" title=" low resource minority language"> low resource minority language</a>, <a href="https://publications.waset.org/abstracts/search?q=fine-tuning" title="fine-tuning">fine-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=dimension%20reduction" title=" dimension reduction"> dimension reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20models" title=" transformer models"> transformer models</a> </p> <a href="https://publications.waset.org/abstracts/145745/evaluation-and-compression-of-different-language-transformer-models-for-semantic-textual-similarity-binary-task-using-minority-language-resources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">211</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12059</span> Sarcasm Recognition System Using Hybrid Tone-Word Spotting Audio Mining Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandhya%20Baskaran">Sandhya Baskaran</a>, <a href="https://publications.waset.org/abstracts/search?q=Hari%20Kumar%20Nagabushanam"> Hari Kumar Nagabushanam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sarcasm%20recognition" title="sarcasm recognition">sarcasm recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=tone-word%20spotting" title=" tone-word spotting"> tone-word spotting</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=pitch%20analyzer" title=" pitch analyzer"> pitch analyzer</a> </p> <a href="https://publications.waset.org/abstracts/71605/sarcasm-recognition-system-using-hybrid-tone-word-spotting-audio-mining-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">293</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12058</span> Selecting Answers for Questions with Multiple Answer Choices in Arabic Question Answering Based on Textual Entailment Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anes%20Enakoa">Anes Enakoa</a>, <a href="https://publications.waset.org/abstracts/search?q=Yawei%20Liang"> Yawei Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title="information retrieval">information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=question%20answering" title=" question answering"> question answering</a>, <a href="https://publications.waset.org/abstracts/search?q=textual%20entailment" title=" textual entailment"> textual entailment</a> </p> <a href="https://publications.waset.org/abstracts/103313/selecting-answers-for-questions-with-multiple-answer-choices-in-arabic-question-answering-based-on-textual-entailment-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103313.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12057</span> Revitalization of Sign Language through Deaf Theatre: A Linguistic Analysis of an Art Form Which Combines Physical Theatre, Poetry, and Sign Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gal%20Belsitzman">Gal Belsitzman</a>, <a href="https://publications.waset.org/abstracts/search?q=Rose%20Stamp"> Rose Stamp</a>, <a href="https://publications.waset.org/abstracts/search?q=Atay%20Citron"> Atay Citron</a>, <a href="https://publications.waset.org/abstracts/search?q=Wendy%20Sandler"> Wendy Sandler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sign languages are considered endangered. The vitality of sign languages is compromised by its unique sociolinguistic situation, in which hearing parents that give birth to deaf children usually decide to cochlear implant their child. Therefore, these children don’t acquire their natural language – Sign Language. Despite this, many sign languages, such as Israeli Sign Language (ISL) are thriving. The continued survival of similar languages under threat has been associated with the remarkable resilience of the language community. In particular, deaf literary traditions are central in reminding the community of the importance of the language. One example of a deaf literary tradition which has received increased popularity in recent years is deaf theatre. The Ebisu Sign Language Theatre Laboratory, developed as part of the multidisciplinary Grammar of the Body Research Project, is the first deaf theatre company in Israel. Ebisu Theatre combines physical theatre and sign language research, to allow for a natural laboratory to analyze the creative use of the body. In this presentation, we focus on the recent theatre production called ‘Their language’ which tells of the struggle faced by the deaf community to use their own natural language in the education system. A thorough analysis unravels how linguistic properties are integrated with the use of poetic devices and physical theatre techniques in this performance, enabling wider access by both deaf and hearing audiences, without interpretation. Interviews with the audience illustrate the significance of this art form which serves a dual purpose, both as empowering for the deaf community and educational for the hearing and deaf audiences, by raising awareness of community-related issues. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deaf%20theatre" title="deaf theatre">deaf theatre</a>, <a href="https://publications.waset.org/abstracts/search?q=empowerment" title=" empowerment"> empowerment</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20revitalization" title=" language revitalization"> language revitalization</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language" title=" sign language"> sign language</a> </p> <a href="https://publications.waset.org/abstracts/99226/revitalization-of-sign-language-through-deaf-theatre-a-linguistic-analysis-of-an-art-form-which-combines-physical-theatre-poetry-and-sign-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99226.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">169</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12056</span> Correlation Analysis to Quantify Learning Outcomes for Different Teaching Pedagogies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kanika%20Sood">Kanika Sood</a>, <a href="https://publications.waset.org/abstracts/search?q=Sijie%20Shang"> Sijie Shang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fundamental goal of education includes preparing students to become a part of the global workforce by making beneficial contributions to society. In this paper, we analyze student performance for multiple courses that involve different teaching pedagogies: a cooperative learning technique and an inquiry-based learning strategy. Student performance includes student engagement, grades, and attendance records. We perform this study in the Computer Science department for online and in-person courses for 450 students. We will perform correlation analysis to study the relationship between student scores and other parameters such as gender, mode of learning. We use natural language processing and machine learning to analyze student feedback data and performance data. We assess the learning outcomes of two teaching pedagogies for undergraduate and graduate courses to showcase the impact of pedagogical adoption and learning outcome as determinants of academic achievement. Early findings suggest that when using the specified pedagogies, students become experts on their topics and illustrate enhanced engagement with peers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bag-of-words" title="bag-of-words">bag-of-words</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20learning" title=" cooperative learning"> cooperative learning</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=inquiry-based%20learning" title=" inquiry-based learning"> inquiry-based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=in-person%20learning" title=" in-person learning"> in-person learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title=" online learning"> online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20pedagogy" title=" teaching pedagogy"> teaching pedagogy</a> </p> <a href="https://publications.waset.org/abstracts/157641/correlation-analysis-to-quantify-learning-outcomes-for-different-teaching-pedagogies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12055</span> Unraveling the Phonosignological Foundations of Human Language and Semantic Analysis of Linguistic Elements in Cross-Cultural Contexts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmudjon%20Kuchkarov">Mahmudjon Kuchkarov</a>, <a href="https://publications.waset.org/abstracts/search?q=Marufjon%20Kuchkarov"> Marufjon Kuchkarov</a>, <a href="https://publications.waset.org/abstracts/search?q=Mukhayyo%20Sobirjanova"> Mukhayyo Sobirjanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The origins of human language remain a profound scientific mystery, characterized by speculative theories often lacking empirical support. This study presents findings that may illuminate the genesis of human language, emphasizing its roots in natural, systematic, and repetitive sound patterns. Also, this paper presents the phonosignological and semantic analysis of linguistic elements across various languages and cultures. By utilizing the principles of the "Human Language" theory, we analyze the symbolic, phonetic, and semantic characteristics of elements such as "A", "L", "I", "F", and "四" (pronounced /si/ in Chinese and /shi/ in Japanese). Our findings reveal that natural sounds and their symbolic representations form the foundation of language, with significant implications for understanding religious and secular myths. This paper explores the intricate relationships between these elements and their cultural connotations, particularly focusing on the concept of "descent" in the context of the phonetic sequence "A, L, I, F," and the symbolic associations of the number four with death. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20research" title="empirical research">empirical research</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20language" title=" human language"> human language</a>, <a href="https://publications.waset.org/abstracts/search?q=phonosignology" title=" phonosignology"> phonosignology</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20patterns" title=" sound patterns"> sound patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=symbolism" title=" symbolism"> symbolism</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20shape" title=" body shape"> body shape</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20language" title=" body language"> body language</a>, <a href="https://publications.waset.org/abstracts/search?q=coding" title=" coding"> coding</a>, <a href="https://publications.waset.org/abstracts/search?q=Latin%20alphabet" title=" Latin alphabet"> Latin alphabet</a>, <a href="https://publications.waset.org/abstracts/search?q=merging%20method" title=" merging method"> merging method</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20sound" title=" natural sound"> natural sound</a>, <a href="https://publications.waset.org/abstracts/search?q=origin%20of%20language" title=" origin of language"> origin of language</a>, <a href="https://publications.waset.org/abstracts/search?q=pairing" title=" pairing"> pairing</a>, <a href="https://publications.waset.org/abstracts/search?q=phonetics" title=" phonetics"> phonetics</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20and%20shape%20production" title=" sound and shape production"> sound and shape production</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20origin" title=" word origin"> word origin</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20semantic" title=" word semantic"> word semantic</a> </p> <a href="https://publications.waset.org/abstracts/188357/unraveling-the-phonosignological-foundations-of-human-language-and-semantic-analysis-of-linguistic-elements-in-cross-cultural-contexts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188357.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">37</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12054</span> On Dialogue Systems Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yifan%20Fan">Yifan Fan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin"> Pingping Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dialogue%20management" title="dialogue management">dialogue management</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20generation" title=" response generation"> response generation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=evaluation" title=" evaluation"> evaluation</a> </p> <a href="https://publications.waset.org/abstracts/129369/on-dialogue-systems-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129369.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">169</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12053</span> Detecting Hate Speech And Cyberbullying Using Natural Language Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N%C3%A1dia%20Pereira">Nádia Pereira</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Ferreira"> Paula Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Francisco"> Sofia Francisco</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Oliveira"> Sofia Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidclay%20Souza"> Sidclay Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Paulino"> Paula Paulino</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Margarida%20Veiga%20Sim%C3%A3o"> Ana Margarida Veiga Simão</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media has progressed into a platform for hate speech among its users, and thus, there is an increasing need to develop automatic detection classifiers of offense and conflicts to help decrease the prevalence of such incidents. Online communication can be used to intentionally harm someone, which is why such classifiers could be essential in social networks. A possible application of these classifiers is the automatic detection of cyberbullying. Even though identifying the aggressive language used in online interactions could be important to build cyberbullying datasets, there are other criteria that must be considered. Being able to capture the language, which is indicative of the intent to harm others in a specific context of online interaction is fundamental. Offense and hate speech may be the foundation of online conflicts, which have become commonly used in social media and are an emergent research focus in machine learning and natural language processing. This study presents two Portuguese language offense-related datasets which serve as examples for future research and extend the study of the topic. The first is similar to other offense detection related datasets and is entitled Aggressiveness dataset. The second is a novelty because of the use of the history of the interaction between users and is entitled the Conflicts/Attacks dataset. Both datasets were developed in different phases. Firstly, we performed a content analysis of verbal aggression witnessed by adolescents in situations of cyberbullying. Secondly, we computed frequency analyses from the previous phase to gather lexical and linguistic cues used to identify potentially aggressive conflicts and attacks which were posted on Twitter. Thirdly, thorough annotation of real tweets was performed byindependent postgraduate educational psychologists with experience in cyberbullying research. Lastly, we benchmarked these datasets with other machine learning classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aggression" title="aggression">aggression</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=cyberbullying" title=" cyberbullying"> cyberbullying</a>, <a href="https://publications.waset.org/abstracts/search?q=datasets" title=" datasets"> datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/142382/detecting-hate-speech-and-cyberbullying-using-natural-language-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">228</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12052</span> Social-Cognitive Aspects of Interpretation: Didactic Approaches in Language Processing and English as a Second Language Difficulties in Dyslexia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Schnell%20Zsuzsanna">Schnell Zsuzsanna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The interpretation of written texts, language processing in the visual domain, in other words, atypical reading abilities, also known as dyslexia, is an ever-growing phenomenon in today’s societies and educational communities. The much-researched problem affects cognitive abilities and, coupled with normal intelligence normally manifests difficulties in the differentiation of sounds and orthography and in the holistic processing of written words. The factors of susceptibility are varied: social, cognitive psychological, and linguistic factors interact with each other. Methods: The research will explain the psycholinguistics of dyslexia on the basis of several empirical experiments and demonstrate how domain-general abilities of inhibition, retrieval from the mental lexicon, priming, phonological processing, and visual modality transfer affect successful language processing and interpretation. Interpretation of visual stimuli is hindered, and the problem seems to be embedded in a sociocultural, psycholinguistic, and cognitive background. This makes the picture even more complex, suggesting that the understanding and resolving of the issues of dyslexia has to be interdisciplinary, aided by several disciplines in the field of humanities and social sciences, and should be researched from an empirical approach, where the practical, educational corollaries can be analyzed on an applied basis. Aim and applicability: The lecture sheds light on the applied, cognitive aspects of interpretation, social cognitive traits of language processing, the mental underpinnings of cognitive interpretation strategies in different languages (namely, Hungarian and English), offering solutions with a few applied techniques for success in foreign language learning that can be useful advice for the developers of testing methodologies and measures across ESL teaching and testing platforms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dyslexia" title="dyslexia">dyslexia</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20cognition" title=" social cognition"> social cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=transparency" title=" transparency"> transparency</a>, <a href="https://publications.waset.org/abstracts/search?q=modalities" title=" modalities"> modalities</a> </p> <a href="https://publications.waset.org/abstracts/165654/social-cognitive-aspects-of-interpretation-didactic-approaches-in-language-processing-and-english-as-a-second-language-difficulties-in-dyslexia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12051</span> Sunspot Cycles: Illuminating Humanity's Mysteries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aghamusa%20Azizov">Aghamusa Azizov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the correlation between solar activity and sentiment in news media coverage, using a large-scale dataset of solar activity since 1750 and over 15 million articles from "The New York Times" dating from 1851 onwards. Employing Pearson's correlation coefficient and multiple Natural Language Processing (NLP) tools—TextBlob, Vader, and DistillBERT—the research examines the extent to which fluctuations in solar phenomena are reflected in the sentiment of historical news narratives. The findings reveal that the correlation between solar activity and media sentiment is generally negligible, suggesting a weak influence of solar patterns on the portrayal of events in news media. Notably, a moderate positive correlation was observed between the sentiments derived from TextBlob and Vader, indicating consistency across NLP tools. The analysis provides insights into the historical impact of solar activity on human affairs and highlights the importance of using multiple analytical methods to understand complex relationships in large datasets. The study contributes to the broader understanding of how extraterrestrial factors may intersect with media-reported events and underlines the intricate nature of interdisciplinary research in the data science and historical domains. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solar%20activity%20correlation" title="solar activity correlation">solar activity correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=media%20sentiment%20analysis" title=" media sentiment analysis"> media sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20event%20patterns" title=" historical event patterns"> historical event patterns</a> </p> <a href="https://publications.waset.org/abstracts/177817/sunspot-cycles-illuminating-humanitys-mysteries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177817.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12050</span> Leveraging Natural Language Processing for Legal Artificial Intelligence: A Longformer Approach for Taiwanese Legal Cases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hsin%20Lee">Hsin Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsuan%20Lee"> Hsuan Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=legal%20artificial%20intelligence" title="legal artificial intelligence">legal artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=computation%20and%20language" title=" computation and language"> computation and language</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20model" title=" language model"> language model</a>, <a href="https://publications.waset.org/abstracts/search?q=Taiwanese%20legal%20cases" title=" Taiwanese legal cases"> Taiwanese legal cases</a> </p> <a href="https://publications.waset.org/abstracts/169638/leveraging-natural-language-processing-for-legal-artificial-intelligence-a-longformer-approach-for-taiwanese-legal-cases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169638.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12049</span> Grounding Chinese Language Vocabulary Teaching and Assessment in the Working Memory Research</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chan%20Kwong%20Tung">Chan Kwong Tung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since Baddeley and Hitch’s seminal research in 1974 on working memory (WM), this topic has been of great interest to language educators. Although there are some variations in the definitions of WM, recent findings in WM have contributed vastly to our understanding of language learning, especially its effects on second language acquisition (SLA). For example, the phonological component of WM (PWM) and the executive component of WM (EWM) have been found to be positively correlated with language learning. This paper discusses two general, yet highly relevant WM findings that could directly affect the effectiveness of Chinese Language (CL) vocabulary teaching and learning, as well as the quality of its assessment. First, PWM is found to be critical for the long-term learning of phonological forms of new words. Second, EWM is heavily involved in interpreting the semantic characteristics of new words, which consequently affects the quality of learners’ reading comprehension. These two ideas are hardly discussed in the Chinese literature, both conceptual and empirical. While past vocabulary acquisition studies have mainly focused on the cognitive-processing approach, active processing, ‘elaborate processing’ (or lexical elaboration) and other effective learning tasks and strategies, it is high time to balance the spotlight to the WM (particularly PWM and EWM) to ensure an optimum control on the teaching and learning effectiveness of such approaches, as well as the validity of this language assessment. Given the unique phonological, orthographical and morphological properties of the CL, this discussion will shed some light on the vocabulary acquisition of this Sino-Tibetan language family member. Together, these two WM concepts could have crucial implications for the design, development, and planning of vocabularies and ultimately reading comprehension teaching and assessment in language education. Hopefully, this will raise an awareness and trigger a dialogue about the meaning of these findings for future language teaching, learning, and assessment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20Language" title="Chinese Language">Chinese Language</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20memory" title=" working memory"> working memory</a>, <a href="https://publications.waset.org/abstracts/search?q=vocabulary%20assessment" title=" vocabulary assessment"> vocabulary assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=vocabulary%20teaching" title=" vocabulary teaching"> vocabulary teaching</a> </p> <a href="https://publications.waset.org/abstracts/75579/grounding-chinese-language-vocabulary-teaching-and-assessment-in-the-working-memory-research" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12048</span> Understanding the Qualitative Nature of Product Reviews by Integrating Text Processing Algorithm and Usability Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cherry%20Yieng%20Siang%20Ling">Cherry Yieng Siang Ling</a>, <a href="https://publications.waset.org/abstracts/search?q=Joong%20Hee%20Lee"> Joong Hee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Myung%20Hwan%20Yun"> Myung Hwan Yun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=usability" title="usability">usability</a>, <a href="https://publications.waset.org/abstracts/search?q=qualitative%20data" title=" qualitative data"> qualitative data</a>, <a href="https://publications.waset.org/abstracts/search?q=text-processing%20algorithm" title=" text-processing algorithm"> text-processing algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/87114/understanding-the-qualitative-nature-of-product-reviews-by-integrating-text-processing-algorithm-and-usability-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87114.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29&page=2" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29&page=2">2</a></li> <li class="page-item active"><span class="page-link">3</span></li> <li class="page-item"><a class="page-link" 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