A ranking approach to keyphrase extraction

ABSTRACT

This paper addresses the issue of automatically extracting keyphrases from a document. Previously, this problem was formalized as classification and learning methods for classification were utilized. This paper points out that it is more essential to cast the problem as ranking and employ a learning to rank method to perform the task. Specifically, it employs Ranking SVM, a state-of-art method of learning to rank, in keyphrase extraction. Experimental results on three datasets show that Ranking SVM significantly outperforms the baseline methods of SVM and Naive Bayes, indicating that it is better to exploit learning to rank techniques in keyphrase extraction.

REFERENCES

• 1: Eibe Frank , Gordon W. Paynter , Ian H. Witten , Carl Gutwin , Craig G. Nevill-Manning, Domain-Specific Keyphrase Extraction, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, p.668-673, July 31-August 06, 1999
• 2: R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers, pages 115--132. MIT Press, 2000.
• 3: P. D. Turney. Learning to extract keyphrases from text. Technical Report ERB-1057, National Research Council, Institute for Information Technology, 2000.
• 4: Endong Xun , Changning Huang , Ming Zhou, A unified statistical model for the identification of English baseNP, Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, p.109-116, October 03-06, 2000, Hong Kong [doi>10.3115/1075218.1075233]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Algorithms, Experimentation

Keywords:
keyphrase extraction, learning to rank, ranking SVM