Generic title labeling for clustered documents

Yuen Hsien Tseng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Document clustering is a powerful technique to detect topics and their relations for information browsing, analysis, and organization. However, clustered documents require post-assignment of descriptive titles to help users interpret the results. Existing techniques often assign labels to clusters based only on the terms that the clustered documents contain, which may not be sufficient for some applications. To solve this problem, a cluster labeling algorithm for creating generic titles, based on external resources such as WordNet, is proposed. Our method first extracts category-specific terms as cluster descriptors. These descriptors are then mapped to generic terms based on a hypernym search algorithm. The proposed method has been evaluated on a patent document collection and a subset of the Reuters-21578 collection. Experimental results revealed that our method performs as anticipated. Real-case applications of these generic terms show promising in assisting humans in interpreting the clustered topics. Our method is general enough such that it can be easily extended to use other hierarchical resources for adaptable label generation.

Original languageEnglish
Pages (from-to)2247-2254
Number of pages8
JournalExpert Systems with Applications
Issue number3
Publication statusPublished - 2010 Mar 15


  • Clustering labeling
  • Correlation coefficient
  • Hypernym search
  • Topic identification
  • WordNet

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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