@inproceedings{7f88d2fc58ef4fc89e71b90e4005ab3c,
title = "One sense per context cluster: Improving word sense disambiguation using web-scale phrase clustering",
abstract = "The performance of word sense disambiguation task is still limited by lexical context matching due to data sparse problem. In this paper we present a simple but effective method that incorporates web-scale phrase clustering results for context matching. This method is able to capture some semantic relations that are not in WordNet. Without using any additional labeled data this new approach obtained 2.11\%-6.92\% higher accuracy over a typical supervised classifier.",
keywords = "Clustering, Web-scale N-grams, Word sense disambiguation",
author = "Heng Ji",
year = "2010",
doi = "10.1109/IUCS.2010.5666225",
language = "English (US)",
isbn = "9781424478200",
series = "2010 4th International Universal Communication Symposium, IUCS 2010 - Proceedings",
pages = "181--184",
booktitle = "2010 4th International Universal Communication Symposium, IUCS 2010 - Proceedings",
note = "2010 4th International Universal Communication Symposium, IUCS 2010 ; Conference date: 18-10-2010 Through 19-10-2010",
}