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Second International Conference Of Language Center At Doha Institute Begins

Second International Conference Of Language Center At Doha Institute Begins
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Data Envelopment Analysis of linguistic features and passage relevance by Bahadorreza Ofoghi, Mahdi Mahdiloo et al

Question Answering (QA) systems play an important role in today's human–computer interaction systems. QA performance can be significantly improved using effective answer passage retrieval and ranking techniques. Our focus in this paper is on both non machine learning-based and deep learning-based passage retrieval and ranking systems for QA to leverage linguistic features within the text of questions and passages and improve passage ranking effectiveness. We propose a decoupled linguistic and linear programming-based approach for passage ranking using the Data Envelopment Analysis (DEA) technique to improve over well-established answer passage retrieval techniques. Our method scores passages using information retrieval and deep learning relevance metrics, represents retrieved passages using their relevance scores and several linguistic features, and finally makes use of DEA to re-rank the retrieved list of passages. The high effectiveness and significance of our proposed passage

Frontiers | To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cybe

When it comes to reporting on sexual assault in media, words matter

When it comes to reporting on sexual assault in media, words matter
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