Title: Learning for Deep Language Understanding Speaker: Smaranda Muresan (Rutgers) Time: 2:15pm-3:30pm, Friday, Nov 11 Place: Room 4102, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Abstract: Recently, several machine learning approaches have been proposed for mapping sentences to their formal meaning representations. However, only a few of them integrate the semantic representation with a grammar formalism, which is needed for linking semantics to other grammatical information, most notably syntax. Lexicalized Well-Founded Grammars (LWFG) is a syntactic-semantic formalism that balances expressiveness with provable learnability results. LWFGs are suited to learning in data-poor settings. In this talk I will introduce several tractable algorithms for LWFG learning, and I will compare them in terms of a-priori knowledge needed by the learner, hypothesis space, algorithm complexity and amount of annotated data needed. I will also briefly present our ontology-based semantic interpreter that can be linked to the grammar through grammar rule constraints, providing access to meaning during parsing and generation. In this approach, the parser will take as input natural language utterances and will produce ontology-based semantic representations. I will show that even with a weak "ontological model", the semantic interpreter at the grammar rule level can help remove erroneous parses obtained when we do not have access to meaning. Speaker's Bio: Smaranda Muresan is an assistant professor in the Library and Information Science Department, School of Communication and Information at Rutgers University. She is the co-director of the Laboratory for the Study of Applied Language Technologies and Society, and a graduate faculty in the department of Computer Science. She received her PhD in Computer Science from Columbia University in 2006. Before coming to Rutgers she was a Postdoctoral Research Associate at the Institute for Advanced Computer Studies at University of Maryland. Her research focuses on computational models for language understanding and learning, as well as language in social media, and is funded primarily by the National Science Foundation (NSF).