| Download | - View accepted manuscript: Word Sense Disambiguation by Web Mining for Word Co-Occurrence Probabilities (PDF, 201 KiB)
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| Author | Search for: Turney, Peter1 |
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| Name affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | The Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (SENSEVAL-3), July 25-26, 2004, Barcelona, Spain |
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| Abstract | 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. |
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| Publication date | 2004 |
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| In | |
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| Language | English |
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| NRC number | NRCC 47167 |
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| NPARC number | 5763802 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | ad3282e8-edb7-4cab-8367-66ad7e02a7eb |
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| Record created | 2009-03-29 |
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| Record modified | 2021-01-05 |
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