• Calendar

December 6, Tuesday
12:00 – 13:30

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. In this talk I will be 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. The first problem I will address is entifying "important" expressions in input text and cross-linking them to Wikipedia pages describing these expressions.This approach allows to enrich the input text with knowledge from Wikipedia. Then, I'll describe an approach for utilizing knowledge imported from Wikipedia for co-reference resolution, the task of understanding that in the expression "Obama addressed the nation, I need your help, he said", ""he" refers to Obama and "your" refers to the American nation.