Enhancing cluster labeling using wikipedia

ABSTRACT

This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The "labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling.

Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster, much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text, pure statistical methods have difficulty in identifying them as good descriptors. Furthermore, our experiments show that for more than 85% of the clusters in our test collection, the manual label (or an inflection, or a synonym of it) appears in the top five labels recommended by our system.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms

Keywords:
cluster labeling, wikipedia