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, exa...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Xiaohua Hu, Xiaodan Zhang, Caimei Lu, E. K. Park, Xiaohua Zhou
• Keywords: Data Mining, Document Representation, KDD 2009, Text Clustering Method, Text Clustering Methods