Title: Knowledge in Natural Languge Processing Speaker: Lev-Arie Ratinov (UIUC) Time: 2pm-3pm, Thursday, Nov 10 Place: Room C203, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Abstract: In the past decade the importance of natural language text processing (NLP) has grown immensely. The first steps in NLP applications involve identification of topics, entities, concepts, and relations in text. Traditionally, statistical models have been successfully deployed for the aforementioned problems. However, the major trend so far has been: scaling up by dumbing down- that is, applying sophisticated statistical algorithms operating on very simple or low-level features of the text. This trend is also exemplified, by expressions such as ``we present a knowledge-lean approach'', which have been traditionally viewed as a positive statement, one that will help papers get into top conferences. In this talk I am making an argument that it is essential to use knowledge in NLP, propose several ways of doing it, and provide case studies on several fundamental NLP problems. I'm planning to give this talk in two parts at CUNY. In part 1, I'll be covering using Wikipedia knowledge for text classification and using knowledge to overcome data sparsity in named entity recognition. In part 2, I'll be covering disambiguating named entities to Wikipedia and using this knowledge to improve co-reference resolution systems. Speaker's Bio: Lev-Arie Ratinov is a Phd student at University of Illinois at Urbana-Champaign in the Cognitive Computation Group. His advisor is Dan Roth. His research interests are in semi-supervised learning, domain adaptation, sentiment analysis and using encyclopedic/external knowledge for the above tasks. He is also interested in applying Machine Learning techniques to many domains, such as natural language processing, finance, medical, information retrieval.