Title :
One sense per context cluster: Improving word sense disambiguation using web-scale phrase clustering
Author_Institution :
Comput. Sci. Dept., City Univ. of New York, New York, NY, USA
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 :
Internet; natural language processing; pattern clustering; Web-scale phrase clustering; WordNet; context cluster; data sparse problem; lexical context matching; supervised classifier; word sense disambiguation task; Clustering algorithms; Clustering methods; Context; Cranes; Magnetic heads; Semantics; Tagging; Clustering; Web-scale N-grams; Word Sense Disambiguation;
Conference_Titel :
Universal Communication Symposium (IUCS), 2010 4th International
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-7821-7
DOI :
10.1109/IUCS.2010.5666225
