Corpus-Based Research on Chinese Language and Linguistics

This volume collects papers presenting corpus-based research on Chinese language and linguistics, from both a synchronic and a diachronic perspective. The contributions cover different fields of linguistics, including syntax and pragmatics, semantics, morphology and the lexicon, sociolinguistics, and corpus building. There is now considerable emphasis on the reliability of linguistic data: the studies presented here are all grounded in the tenet that corpora, intended as collections of naturally occurring texts produced by a variety of speakers/writers, provide a more robust, statistically significant foundation for linguistic analysis. The volume explores not only the potential of using corpora as tools allowing access to authentic language material, but also the challenges involved in corpus interrogation, analysis, and building

EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop

Editor of the proceedings of EVALITA 2016

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations

Fake News and Social Media: The Impact of Emotional Lexicon on Interactive Behaviors

As issues with fake news continue to increase, so does the need to understand better the motivation for interacting with these types of articles. Social media has become a primary source for finding news. Individuals within social media have the option to share, like, and comment on new articles. Interventions such as fake checkers, rater comments, and other types of warnings have been proven helpful in slowing the believability and interactive behaviors of fake news articles on social media sites. This qualitative, phenomenological study interviewed five participants to gain insight into how individuals experience the negative emotional lexicon within fake news articles and interact with these articles on social media. A modified Van Kaam analysis method found that the sample population does not interact (like, share, or comment) on fake news articles regardless of the language used. The study\u27s findings also found that individuals feel compelled to discuss the topic in face-to-face settings, highlighting the importance of information sharing while avoiding digital platforms as the mechanism for achieving the goal