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EMNLP
2007

Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information

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Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information
We present results that show that incorporating lexical and structural semantic information is effective for word sense disambiguation. We evaluated the method by using precise information from a large treebank and an ontology automatically created from dictionary sentences. Exploiting rich semantic and structural information improves precision 2–3%. The most gains are seen with verbs, with an improvement of 5.7% over a model using only bag of words and n-gram features.
Takaaki Tanaka, Francis Bond, Timothy Baldwin, San
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Takaaki Tanaka, Francis Bond, Timothy Baldwin, Sanae Fujita, Chikara Hashimoto
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