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Tweeting beyond Facts – The Need for a Linguistic Perspective

 

After a short summer break Data Science Society is back with another Data Science Talk in collaboration with our friends from Mozaika who will introduce Professor Sabine Bergler from Concordia University, Montreal, Canada to the Bulgarian audience.

Topic: Tweeting Beyond Facts – The Need for a Linguistic Perspective
Speaker: Dr. Sabine Bergler – Concordia University, Montreal, Canada

Bio: Doctor Sabine Bergler is a Full Professor at the Department of Computer Science at Concordia University, Montreal, Canada. She holds a PhD from Brandeis University, Boston, USA, on reported speech and has degrees from the University of Massachusetts at Amherst and the University of Stuttgart. She founded the CLaC Laboratory in 2002 at Concordia, where she conducts research on computational linguistics. Some of the important highlights of this research include groundbreaking work on sentiment analysis and embedding predicates as unified theoretical foundation in language semantics that have been carried out under her direction. Her students consistently win competitions on speculative language in bioNLP at BioNLP, on negation focus and modality at *Sem and at QA4MRE and on sentiment analysis at SemEval.

Time: Thursday, September 10, 19:00
Registration starts at 18:30
Place: Sofia University, New Conference Hall (alias Mirror Hall)

Abstract:

Text is only accepted by its intended audience when included facts are properly anchored in extra-propositional information: source, time, date, sentiment, certainty, veridicity, etc. can all be conveyed through linguistic embedding constructions, among others. Work in the CLaC Lab has a long tradition of modeling this extra-propositional material, from reported speech to speculative language, negation, modality, event temporal anchoring and tense information. I show results from our recent validation of negation and modality as contributing to a downstream task of sentiment analysis of tweets and outline how I consider this validation to extend to our continuing effort to give a modular, shallow, and compositional treatment of embedding predicates in general.

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