Domain-specific keyphrase extraction

  • Authors:

  • Eibe Frank;Gordon W. Paynter;Ian H. Witten;Carl Gutwin;Craig G. Nevill-Manning

  • Affiliations:

  • Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Saskatchewan, Saskatoon, Canada;Department of Computer Science, Rutgers University, Piscataway, New Jersey

  • Venue:
  • IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1999

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Abstract

Keyphrases are an important means of document summarization, clustering, and topic search. Only a small minority of documents have author-assigned keyphrases, and manually assigning keyphrases to existing documents is very laborious. Therefore it is highly desirable to automate the keyphrase extraction process. This paper shows that a simple procedure for keyphrase extraction based on the naive Bayes learning scheme performs comparably to the state of the art. It goes on to explain how this procedure's performance can be boosted by automatically tailoring the extraction process to the particular document collection at hand. Results on a large collection of technical reports in computer science show that the quality of the extracted keyphrases improves significantly when domain-specific information is exploited.