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SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation

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Book cover Advances in Computational Intelligence Systems (UKCI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 650))

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Abstract

Keyphrases provide important semantic metadata for organizing and managing free-text documents. As data grow exponentially, there is a pressing demand for automatic and efficient keyphrase extraction methods. We introduce in this paper SemCluster, a clustering-based unsupervised keyphrase extraction method. By integrating an internal ontology (i.e., WordNet) with external knowledge sources, SemCluster identifies and extracts semantically important terms from a given document, clusters the terms, and, using the clustering results as heuristics, identifies the most representative phrases and singles them out as keyphrases. SemCluster is evaluated against two baseline unsupervised methods, TextRank and KeyCluster, over the Inspec dataset under an F1-measure metric. The evaluation results clearly show that SemCluster outperforms both methods.

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Notes

  1. 1.

    http://www.dpedia.org.

  2. 2.

    http://yago-knowledge.org.

  3. 3.

    DBpedia schema is available at http://mappings.dbpedia.org/server/ontology/classes/.

  4. 4.

    The alignment results are available at http://www.pdlab.io/semcluster/inspec.rar.

  5. 5.

    The lookup code is available at https://github.com/dbpedia/lookup.

  6. 6.

    ESA code is available at http://treo.deri.ie/easyesa/.

  7. 7.

    http://opennlp.apache.org.

  8. 8.

    Wordnet v.3.1 is available at https://wordnet.princeton.edu/wordnet/download.

  9. 9.

    Hulth’s dataset copy is available at http://pdllab.io/semcluster/inspec.rar.

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Authors and Affiliations

  1. School of Computer Science and Informatics, Cardiff University, Cardiff, UK

    Hassan H. Alrehamy & Coral Walker

Authors
  1. Hassan H. Alrehamy
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  2. Coral Walker
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Correspondence to Coral Walker .

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Editors and Affiliations

  1. Xiamen University, Xiamen Shi, Fujian, China

    Fei Chao

  2. Cardiff University, Cardiff, United Kingdom

    Steven Schockaert

  3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

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Alrehamy, H.H., Walker, C. (2018). SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-66939-7_19

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  • Online ISBN: 978-3-319-66939-7

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