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SemCluster: Unsupervised automatic keyphrase extraction using affinity propagation

Alrehamy, Hassan H. and Walker, Coral ORCID: 2018. SemCluster: Unsupervised automatic keyphrase extraction using affinity propagation. Presented at: 17th UK Workshop on Computational Intelligence, Cardiff, UK, 6-8 September 2017. Published in: Chao, Fei, Schockaert, Steven ORCID: and Zhang, Qingfu eds. Advances in Computational Intelligence Systems: UKCI 2017. Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing , vol.650 Cham: Springer, pp. 222-235. 10.1007/978-3-319-66939-7_19

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer
ISBN: 978-3-319-66939-7
ISSN: 2194-5357
Last Modified: 22 Apr 2023 14:02

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