Word sense disambiguation incorporating lexical and structural semantic information
Date of Issue2007
Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (2007 : Prague)
School of Humanities and Social Sciences
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.
© 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].