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EMNLP
2004

Unsupervised WSD based on Automatically Retrieved Examples: The Importance of Bias

9 years 7 months ago
Unsupervised WSD based on Automatically Retrieved Examples: The Importance of Bias
This paper explores the large-scale acquisition of sense-tagged examples for Word Sense Disambiguation (WSD). We have applied the "WordNet monosemous relatives" method to construct automatically a web corpus that we have used to train disambiguation systems. The corpus-building process has highlighted important factors, such as the distribution of senses (bias). The corpus has been used to train WSD algorithms that include supervised methods (combining automatic and manuallytagged examples), minimally supervised (requiring sense bias information from hand-tagged corpora), and fully unsupervised. These methods were tested on the Senseval-2 lexical sample test set, and compared successfully to other systems with minimum or no supervision.
Eneko Agirre, David Martínez
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where EMNLP
Authors Eneko Agirre, David Martínez
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