Title

Instance Weighting for Domain Adaptation in NLP

Publication Type

Conference Proceeding Article

Publication Date

6-2007

Abstract

Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting per- spective. We formally analyze and charac- terize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, cor- responding to the different distributions of instances and classification functions in the source and the target domains. We then propose a general instance weighting frame- work for domain adaptation. Our empir- ical results on three NLP tasks show that incorporating and exploiting more informa- tion from the target domain through instance weighting is effective.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

45th Annual Meeting of the Association Computational Linguistics (ACL'07)

First Page

264

Last Page

271

City or Country

Prague, Czech

Additional URL

http://clair.si.umich.edu/clair/anthology/query.cgi?type=Paper&id=P07-1034

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