Towards a Form-independent Semantics for NLP

Title: Towards a Form-independent Semantics for NLP
Speaker: Prof. Mark Steedman, University of Edinburgh (FBA, FRSE, Fellow of ACL, CSS and AAAI) 
Time: 2:00PM  to  3:00PM, Tuesday, 13th November 
Location: Room 106, ICST Building 
Abstract:  The central problem in open-domain question-answering from text is that the linguistic form in which a question is asked is rarely the same as the form of the text that answers it.  I'll report progress on a project that aims to define a form- and language- independent meaning representation by building entailment graphs over relations automatically extracted from large amounts of text using wide-coverage parsers. 
Speaker's Bio: 
Mark Steedman is Professor of Cognitive Science in the School of Informatics at the University of Edinburgh, to which he moved in 1998 from the University of Pennsylvania, where he taught for many years as Professor in the Department of Computer and Information Science. He is a Fellow of the British Academy, the Royal Society of Edinburgh, the American Association for Artificial Intelligence (AAAI), the Association for Computational Linguistics (ACL), and the Cognitive Science Society (CSS), and a Member of the European Academy. In 2018, he was the recipient of the ACL Lifetime Achievement Award. 
His research covers a wide range of problems in computational linguistics, natural language processing, artificial intelligence, and cognitive science, including syntactic and semantic theory, and parsing and interpretation of natural language text and discourse, including spoken intonation, by humans and by machine. Much of his current research uses Combinatory Categorial Grammar (CCG) as a formalism to address problems in wide-coverage parsing for robust semantic interpretation and natural language inference, and the problem of inducing and generalizing semantic parsers, both from data and in child language acquisition. Some of his research concerns the analysis of music using related grammars and statistical parsing models. 

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