Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78273
Title: Optimizing classifier performance in word sense disambiguation by redefining word sense classes
Authors: Kohomban, U.S.
Lee, W.S. 
Issue Date: 2007
Source: Kohomban, U.S.,Lee, W.S. (2007). Optimizing classifier performance in word sense disambiguation by redefining word sense classes. IJCAI International Joint Conference on Artificial Intelligence : 1635-1640. ScholarBank@NUS Repository.
Abstract: Learning word sense classes has been shown to be useful in fine-grained word sense disambiguation [Kohomban and Lee, 2005]. However, the common choice for sense classes, WordNet lexicographer files, are not designed for machine learning based word sense disambiguation. In this work, we explore the use of clustering techniques in an effort to construct sense classes that are more suitable for word sense disambiguation end-task. Our results show that these classes can significantly improve classifier performance over the state of the art results of unrestricted word sense disambiguation.
Source Title: IJCAI International Joint Conference on Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/78273
ISSN: 10450823
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

35
checked on Jan 21, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.