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

Aman, M. and Abdulkadir, S.J. and Aziz, I.A. and Alhussian, H. and Ullah, I. (2021) KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data. Multimedia Tools and Applications, 80 (8). pp. 12469-12506.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Benchmarking; Digital libraries; Information retrieval; Semantics, Clustering techniques; Concept identification; Context dependency; Keyphrase extraction; Latent Semantic Analysis; Performance measure; Semantic similarity; Unsupervised approaches, Data mining
ID Code:23821
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 13:09
Last Modified:19 Aug 2021 13:09

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