Publication Type
Conference Proceeding Article
Version
acceptedVersion
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 Science and Engineering
Publication
ACL 2007: Proceedings of the 45th Annual Meeting of the Association Computational Linguistics, Prague; Czech Republic, June 23-30
First Page
264
Last Page
271
Publisher
ACL
City or Country
Philadelphia, PA
Citation
JIANG, Jing and ZHAI, ChengXiang.
Instance Weighting for Domain Adaptation in NLP. (2007). ACL 2007: Proceedings of the 45th Annual Meeting of the Association Computational Linguistics, Prague; Czech Republic, June 23-30. 264-271.
Available at: https://ink.library.smu.edu.sg/sis_research/1253
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://aclanthology.coli.uni-saarland.de/papers/P07-1034/p07-1034
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons