Information Extraction from Online Text — from Opinions to Arguments to Persuasion
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A long line of research in Natural Language Processing (NLP), including our own, has addressed the task of identifying and extracting information about opinions with the goal of determining what people (and other entities) are thinking or feeling. In this talk, I'll present new research on argument mining, a relatively new area of study in NLP that focuses less on extracting from text WHAT people think or feel, but rather analyzing argumentative text to understand WHY they do so. Specifically, I will first present some of our new research on the automatic analysis of informal, user-generated arguments in which we aim to expose the intended underlying structure of the argument. Next, I'll present our research that examines arguments on a public debate forum to understand what makes one argument more convincing than another.
Claire Cardie is the John C. Ford Professor of Engineering in the Departments of Computer Science and Information Science at Cornell University. She was the founding Chair of Cornell's Information Science Department and has worked in the area of Natural Language Processing (NLP) (for longer than she would like to admit) on topics ranging from information extraction, text summarization and noun phrase coreference resolution to the automatic analysis of opinions, sentiment and deception in text. Cardie was selected as a Fellow of the Association for Computational Linguistics in 2015. She has served on the executive committees of the ACL, NAACL and AAAI, and has been Program Chair for EMNLP, CoNLL, ACL and COLING as well as General Chair this past July for ACL 2018 in Melbourne.