The necessity of combining adaptation methods

Ming Wei Chang, Michael Connor, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Problems stemming from domain adaptation continue to plague the statistical natural language processing community. There has been continuing work trying to find general purpose algorithms to alleviate this problem. In this paper we argue that existing general purpose approaches usually only focus on one of two issues related to the difficulties faced by adaptation: 1) difference in base feature statistics or 2) task differences that can be detected with labeled data. We argue that it is necessary to combine these two classes of adaptation algorithms, using evidence collected through theoretical analysis and simulated and real-world data experiments. We find that the combined approach often outperforms the individual adaptation approaches. By combining simple approaches from each class of adaptation algorithm, we achieve state-of-the-art results for both Named Entity Recognition adaptation task and the Preposition Sense Disambiguation adaptation task. Second, we also show that applying an adaptation algorithm that finds shared representation between domains often impacts the choice in adaptation algorithm that makes use of target labeled data.

Original languageEnglish (US)
Title of host publicationEMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Pages767-777
Number of pages11
StatePublished - Dec 1 2010
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2010 - Cambridge, MA, United States
Duration: Oct 9 2010Oct 11 2010

Publication series

NameEMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Other

OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2010
CountryUnited States
CityCambridge, MA
Period10/9/1010/11/10

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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