When: Friday, May 16, 2:00 p.m.- 3:00 p.m.

Where: 1305 Newell-Simon Hall

Jing Jiang, University of Illinois at Urbana-Champaign

LTI Seminar

Abstract:
With the explosion of the amount of textual data in the information age, natural language processing has become increasingly important, with direct applications in areas such as Web mining and biomedical literature mining. Currently, the most effective approach to solving many NLP problems is supervised learning coupled with linguistic knowledge. However, standard supervised learning requires the training and the test corpora to be similar, and therefore falls apart in real NLP applications because obtaining labeled data for every new domain is expensive and thus infeasible. In this talk, I will focus on the major line of my PhD research on domain adaptation in NLP, which aims at adapting classifiers trained on one domain to another domain. We have proposed two frameworks to achieve domain adaptation, one based on instance weighting and the other based on feature selection. Both frameworks have been evaluated on real NLP problems, and shown to be effective compared with standard learning methods.

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