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SemGraph: Extracting keyphrases following a novel semantic graph-based approach

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  • Juan Martinez-Romo
  • Lourdes Araujo
  • Andres Duque Fernandez

Abstract

type="main"> Keyphrases represent the main topics a text is about. In this article, we introduce SemGraph, an unsupervised algorithm for extracting keyphrases from a collection of texts based on a semantic relationship graph. The main novelty of this algorithm is its ability to identify semantic relationships between words whose presence is statistically significant. Our method constructs a co-occurrence graph in which words appearing in the same document are linked, provided their presence in the collection is statistically significant with respect to a null model. Furthermore, the graph obtained is enriched with information from WordNet. We have used the most recent and standardized benchmark to evaluate the system ability to detect the keyphrases that are part of the text. The result is a method that achieves an improvement of 5.3% and 7.28% in F measure over the two labeled sets of keyphrases used in the evaluation of SemEval-2010.

Suggested Citation

  • Juan Martinez-Romo & Lourdes Araujo & Andres Duque Fernandez, 2016. "SemGraph: Extracting keyphrases following a novel semantic graph-based approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(1), pages 71-82, January.
    • Handle: RePEc:bla:jinfst:v:67:y:2016:i:1:p:71-82
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    File URL: http://hdl.handle.net/10.1002/asi.23365
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