Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/92286
Title: Word sense disambiguation incorporating lexical and structural semantic information
Authors: Tanaka, Takaaki
Bond, Francis
Baldwin, Timothy
Fujita, Sanae
Hashimoto, Chikara
Keywords: DRNTU::Humanities::Language::Japanese
DRNTU::Humanities::Linguistics::Sociolinguistics::Computational linguistics
Issue Date: 2007
Source: Tanaka, T., Bond, F., Baldwin, T., Fujita, S., & Hashimoto, C. (2007). Word sense disambiguation incorporating lexical and structural semantic information. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Abstract: 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.
URI: https://hdl.handle.net/10356/92286
http://hdl.handle.net/10220/6449
Rights: © 2007 ACL This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Association for Computational Linguistics. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [URL: http://www.aclweb.org/anthology-new/D/D07/D07-1050.pdf].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:HSS Conference Papers

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