2009 ExploitingWikipediaAsExternalKn

Jump to: navigation, search

Subject Headings: Text Clustering Algorithm, Document Data Record, Wikipedia-based Word Mention Normalization Algorithm.


Cited By


Author Keywords


In traditional text clustering methods, documents are represented asbags of words” without considering the semantic information of each document. For instance, if two documents use different collections of core words to represent the same topic, they may be falsely assigned to different clusters due to the lack of shared core words, although the core words they use are probably synonyms or semantically associated in other forms. The most common way to solve this problem is to enrich document representation with the background knowledge in an ontology. There are two major issues for this approach: (1) the coverage of the ontology is limited, even for WordNet or Mesh, (2) using ontology terms as replacement or additional features may cause information loss, or introduce noise. In this paper, we present a novel text clustering method to address these two issues by enriching document representation with Wikipedia concept and category information. We develop two approaches, exact match and relatedness-match, to map text documents to Wikipedia concepts, and further to Wikipedia categories. Then the text documents are clustered based on a similarity metric which combines document content information, concept information as well as category information. The experimental results using the proposed clustering framework on three datasets (20-newsgroup, TDT2, and LA Times) show that clustering performance improves significantly by enriching document representation with Wikipedia concepts and categories.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 ExploitingWikipediaAsExternalKnXiaohua Hu
Xiaodan Zhang
Caimei Lu
E. K. Park
Xiaohua Zhou
Exploiting Wikipedia As External Knowledge for Document ClusteringKDD-2009 Proceedings10.1145/1557019.15570662009