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Semantic Measures for Keywords Extraction

  • Davide Colla
  • Enrico Mensa
  • Daniele P. RadicioniEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10640)

Abstract

In this paper we introduce a minimalist hypothesis for keywords extraction: keywords can be extracted from text documents by considering concepts underlying document terms. Furthermore, central concepts are individuated as the concepts that are more related to title concepts. Namely, we propose five metrics, that are diverse in essence, to compute the centrality of concepts in the document body with respect to those in the title. We finally report about an experimentation over a popular data set of human annotated news articles; the results confirm the soundness of our hypothesis.

Keywords

Keywords extraction Natural language semantics Conceptual similarity Word similarity Lexical resources 

Notes

Acknowledgements

We desire to thank Simone Donetti and the Technical Staff of the Computer Science Department of the University of Turin, for their support in the setup and administration of the computer system used in the experimentation.

The authors are also grateful to the anonymous reviewers for their valuable comments and suggestions.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Davide Colla
    • 1
  • Enrico Mensa
    • 1
  • Daniele P. Radicioni
    • 1
    Email author
  1. 1.Dipartimento di InformaticaUniversità di TorinoTurinItaly

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