document: Exploiting Wikipedia as external knowledge for document clustering In traditional text clustering methods , documents are represented as `` bags 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 .
doc_bio_tags: O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O B O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O O