CorePhrase: keyphrase extraction for document clustering

  • Authors:

  • Khaled M. Hammouda;Diego N. Matute;Mohamed S. Kamel

  • Affiliations:

  • Department of Systems Design Engineering;School of Computer Science;Department of Electrical and Computer Engineering, Pattern Analysis and Machine Intelligence (PAMI) Research Group, University of Waterloo, Waterloo, ON, Canada

  • Venue:
  • MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

The ability to discover the topic of a large set of text documents using relevant keyphrases is usually regarded as a very tedious task if done by hand. Automatic keyphrase extraction from multi-document data sets or text clusters provides a very compact summary of the contents of the clusters, which often helps in locating information easily. We introduce an algorithm for topic discovery using keyphrase extraction from multi-document sets and clusters based on frequent and significant shared phrases between documents. The keyphrases extracted by the algorithm are highly accurate and fit the cluster topic. The algorithm is independent of the domain of the documents. Subjective as well as quantitative evaluation show that the algorithm outperforms keyword-based cluster-labeling algorithms, and is capable of accurately discovering the topic, and often ranking it in the top one or two extracted keyphrases.