Paper: Instance Weighting for Domain Adaptation in NLP

ACL ID P07-1034
Title Instance Weighting for Domain Adaptation in NLP
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

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.