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CORR
2004
Springer

Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities

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Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities
This paper describes the National Research Council (NRC) Word Sense Disambiguation (WSD) system, as applied to the English Lexical Sample (ELS) task in Senseval-3. The NRC system approaches WSD as a classical supervised machine learning problem, using familiar tools such as the Weka machine learning software and Brill's rule-based part-of-speech tagger. Head words are represented as feature vectors with several hundred features. Approximately half of the features are syntactic and the other half are semantic. The main novelty in the system is the method for generating the semantic features, based on word co-occurrence probabilities. The probabilities are estimated using the Waterloo MultiText System with a corpus of about one terabyte of unlabeled text, collected by a web crawler.
Peter D. Turney
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2004
Where CORR
Authors Peter D. Turney
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