Domain Adaptation for Statistical Classifiers

Details Domain Adaptation for Statistical Classifiers Domain Adaptation for Statistical Classifiers

2011-09-28

arxiv.org/abs/1109.6341v1

Journal Of Artificial Intelligence Research, Volume 26, pages 101-126, 2006

Computer Science

Learning

Scientific paper

10.1613/jair.1872

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.

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