Research Area:  Machine Learning

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.

Keywords:  
Keyphrase extraction
Key concept extraction
Information retrieval
Information extraction
Text mining
Machine Learning
Deep Learning

Author(s) Name:  Muhammad Aman, Said Jadid Abdulkadir, Izzatdin Abdul Aziz, Hitham Alhussian & Israr Ullah

Publisher name:  Springer

DOI:  10.1007/s11042-020-10215-x

Volume Information:  volume 80, pages: 12469–12506 (2021)

Paper Link:   https://link.springer.com/article/10.1007/s11042-020-10215-x