Combining heterogeneous classifiers for word-sense disambiguation

Authors:
H. Tolga Ilhan;Sepandar D. Kamvar;Dan Klein;Christopher D. Manning;Kristina Toutanova
Affiliations:
Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA
Venue:
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Year:
2001

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Abstract

The Stanford-CS224N system is an ensemble of simple classifiers. The first-tier systems are heterogeneous, consisting primarily of naive-Bayes variants, but also including vector space, memory-based, and other classifier types. These simple classifiers are combined by a second-tier classifier, which variously uses majority voting, weighted voting, or a maximum entropy model. Results from Senseval-2 lexical sample tasks indicate that, while the individual classifiers perform at a level comparable to middle-scoring team's systems, the combination achieves high performance. In this paper, we discuss both our system and lessons learned from its behavior.