NDLI: Learning word sense disambiguation in biomedical text with difference between training and test distributions

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Learning word sense disambiguation in biomedical text with difference between training and test distributions

Content Provider	ACM Digital Library
Author	Son, Jeong-Woo Park, Seong-Bae
Abstract	Word sense disambiguation (WSD) is a crucial issue in bio-medical text mining since the performance of diverse biomedical text mining techniques strongly depends on the senses of lexicons. Thus, it is natural to consider lexicons as the most crucial features in WSD. However, due to the diversity of lexical space, WSD methods based on machine learning techniques with lexical features suffer from the difference between distributions of training and test documents. To tackle this problem, this paper proposes support vector machines with example-wise weights. In this method, the training distribution is made coincide with the test distribution by weighting training examples according to their similarity to all test data. The experimental results show that the distribution change between training and test data is actually recognized and the proposed method which considers this change in its training phase outperforms ordinary support vector machines.
Starting Page	59
Ending Page	66
Page Count	8
File Format	PDF
ISBN	9781605588032
DOI	10.1145/1651318.1651330
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-11-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Biotext classification Machine learning Covariate shift Distribution change Kernel method
Content Type	Text
Resource Type	Article

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