Word Sense Disambiguation of semantic document

Author

Shi, Bin ; Fang, Liying ; Yan, Jianzhuo ; Wang, Pu

Author_Institution

Coll. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China

Volume

3

fYear

2010

fDate

21-24 May 2010

Abstract

A Max-Probability Density based Clustering (MPDC) algorithm is proposed in this paper to resolve the problem of Word Sense Disambiguation in semantic document. MPDC take the context information of a keyword based on WordNet into account and select the max probability sense by measuring the density of the concept. We also do experiment on semantic documents retrieving from Swoogle and Watson, two famous semantic web searching engines. The result shows MPDC get a good efficiency.

Keywords

information retrieval; natural language processing; pattern clustering; search engines; semantic Web; Swoogle; Watson; WordNet; max-probability density based clustering algorithm; semantic Web searching engines; semantic document retrieval; word sense disambiguation; Clustering algorithms; Control engineering; Data mining; Density measurement; Educational institutions; Frequency; Intelligent systems; Natural language processing; Search engines; Semantic Web; Density based Clustering; WordNet; word sense disambiguation;

fLanguage

English

Publisher

ieee

Conference_Titel

Future Computer and Communication (ICFCC), 2010 2nd International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5821-9

Type

conf

DOI

10.1109/ICFCC.2010.5497655

Filename

5497655