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Conference Papers Year : 2011

A Word Clustering Approach to Domain Adaptation: Effective Parsing of Biomedical Texts

Abstract

We present a simple and effective way to perform out-of-domain statistical parsing by drastically reducing lexical data sparseness in a PCFG-LA architecture. We replace terminal symbols with unsupervised word clusters acquired from a large newspaper corpus augmented with biomedical target- domain data. The resulting clusters are effective in bridging the lexical gap between source-domain and target-domain vocabularies. Our experiments combine known self-training techniques with unsupervised word clustering and produce promising results, achieving an error reduction of 21% on a new evaluation set for biomedical text with manual bracketing annotations.
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Dates and versions

hal-00659577 , version 1 (13-01-2012)

Identifiers

  • HAL Id : hal-00659577 , version 1

Cite

Marie Candito, Enrique Henestroza Anguiano, Djamé Seddah. A Word Clustering Approach to Domain Adaptation: Effective Parsing of Biomedical Texts. IWPT'11 - 12th International Conference on Parsing Technologies, Oct 2011, Dublin, Ireland. ⟨hal-00659577⟩
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