Combining Classifiers for word sense disambiguation

Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool whic...

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Bibliographic Details
Published in:Natural language engineering 2002-12, Vol.8 (4), p.327-341
Main Authors: FLORIAN, RADU, CUCERZAN, SILVIU, SCHAFER, CHARLES, YAROWSKY, DAVID
Format: Article
Language:English
Subjects:
Children & youth
Parameter estimation
Word sense disambiguation
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Summary:Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Naïve Bayes, Cosine, Decision List, Transformation-based Learning and MMVC classifiers. Each classifier has access to the same rich feature space, comprised of distance weighted bag-of-lemmas, local ngram context and specific syntactic relations, such as Verb-Object and Noun-Modifier. This study examines several key issues in system combination for the word sense disambiguation task, ranging from algorithmic structure to parameter estimation. Experiments using the standard SENSEVAL2 lexical-sample data sets in four languages (English, Spanish, Swedish and Basque) demonstrate that the combination system obtains a significantly lower error rate when compared with other systems participating in the SENSEVAL2 exercise, yielding state-of-the-art performance on these data sets.
ISSN:1351-3249
1469-8110
DOI:10.1017/S1351324902002978