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.
REF explore the large-scale acquisition of sense-tagged examples from the Web and train supervised, minimally supervised (requiring sense bias information from handtagged corpora, similar to our system), and fully unsupervised WSD algorithms using this corpus.
Unsupervised WSD Based On Automatically Retrieved Examples: The Importance Of Bias
SIGDAT Conference On Empirical Methods In Natural Language Processing
Computer Science