Keyphrase extraction in scientific publications

  • Authors:
  • Thuy Dung Nguyen;Min-Yen Kan

  • Affiliations:
  • Department of Computer Science, School of Computing, National University of Singapore, Singapore;Department of Computer Science, School of Computing, National University of Singapore, Singapore

  • Venue:
  • ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present a keyphrase extraction algorithm for scientific publications. Different from previous work, we introduce features that capture the positions of phrases in document with respect to logical sections found in scientific discourse. We also introduce features that capture salient morphological phenomena found in scientific keyphrases, such as whether a candidate keyphrase is an acronyms or uses specific terminologically productive suffixes. We have implemented these features on top of a baseline feature set used by Kea [1]. In our evaluation using a corpus of 120 scientific publications multiply annotated for keyphrases, our system significantly outperformed Kea at the p