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Domain Adaptation for Statistical Classifiers

The Journal of artificial intelligence research, 2006-01, Vol.26, p.101-126 [Peer Reviewed Journal]

2006. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.1872

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  • Title:
    Domain Adaptation for Statistical Classifiers
  • Author: Daume III, H. ; Marcu, D.
  • Subjects: Algorithms ; Artificial intelligence ; Classifiers ; Domains ; Learning theory ; Maximum entropy ; Natural language processing ; Probabilistic models ; Training
  • Is Part Of: The Journal of artificial intelligence research, 2006-01, Vol.26, p.101-126
  • Description: The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.
  • Publisher: San Francisco: AI Access Foundation
  • Language: English
  • Identifier: ISSN: 1076-9757
    EISSN: 1943-5037
    DOI: 10.1613/jair.1872
  • Source: Freely Accessible Journals
    Alma/SFX Local Collection
    ProQuest Central
    DOAJ Directory of Open Access Journals
    American Association for Artificial Intelligence publications
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