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KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

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Abstract

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

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Acknowledgments

This research was fully funded and supported by Universiti Teknologi PETRONAS, under the Yayasan Universiti Teknologi PETRONAS (YUTP), Cost Centre (015LC0-119).

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Muhammad Aman

  2. Technology and Development Directorate, National Database and Registration Authority (NADRA), Islamabad, Pakistan

    Muhammad Aman

  3. Center for Research in Data Science (CeRDaS), Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Said Jadid Abdulkadir, Izzatdin Abdul Aziz & Hitham Alhussian

  4. Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan

    Israr Ullah

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  1. Muhammad Aman
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Correspondence to Said Jadid Abdulkadir.

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Appendix: Description of part-of-speech (POS) tags

Appendix: Description of part-of-speech (POS) tags

Table 15 Description of part-of-speech (POS) tags [37]
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Aman, M., Abdulkadir, S.J., Aziz, I.A. et al. KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data. Multimed Tools Appl 80, 12469–12506 (2021). https://doi.org/10.1007/s11042-020-10215-x

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