MFSRank: An Unsupervised Method to Extract Keyphrases Using Semantic Information

  • Roque Enrique López
  • Dennis Barreda
  • Javier Tejada
  • Ernesto Cuadros
Conference paper

DOI: 10.1007/978-3-642-25324-9_29

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)
Cite this paper as:
López R.E., Barreda D., Tejada J., Cuadros E. (2011) MFSRank: An Unsupervised Method to Extract Keyphrases Using Semantic Information. In: Batyrshin I., Sidorov G. (eds) Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science, vol 7094. Springer, Berlin, Heidelberg

Abstract

This paper presents an unsupervised graph-based method to extract keyphrases using semantic information. The proposed method has two stages. In the first one, we have extracted MFS (Maximal Frequent Sequences) and built the nodes of a graph with them. The weight of the connection between two nodes has been established according to common statistical information and semantic relatedness. In the second stage, we have ranked MFS with traditionally PageRank algorithm; but we have included ConceptNet. This external resource adds an extra weight value between two MFS. The experimental results are competitive with traditional approaches developed in this area. MFSRank overcomes the baseline for top 5 keyphrases in precision, recall and F-score measures.

Keywords

Keyphrase Extraction Maximal frequent sequences Semantic Graphs 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Roque Enrique López
    • 1
  • Dennis Barreda
    • 2
  • Javier Tejada
    • 2
  • Ernesto Cuadros
    • 2
  1. 1.School of System EngineeringSan Agustin National UniversityPerú
  2. 2.School of Computer ScienceSan Pablo Catholic UniversityPerú

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