Evaluating N-gram based evaluation metrics for automatic keyphrase extraction

  • Authors:
  • Su Nam Kim;Timothy Baldwin;Min-Yen Kan

  • Affiliations:
  • University of Melbourne;University of Melbourne;National University of Singapore

  • Venue:
  • COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
  • Year:
  • 2010

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Abstract

This paper describes a feasibility study of n-gram-based evaluation metrics for automatic keyphrase extraction. To account for near-misses currently ignored by standard evaluation metrics, we adapt various evaluation metrics developed for machine translation and summarization, and also the R-precision evaluation metric from keyphrase evaluation. In evaluation, the R-precision metric is found to achieve the highest correlation with human annotations. We also provide evidence that the degree of semantic similarity varies with the location of the partially-matching component words.