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Key concept identification: A comprehensive analysis of frequency and topical graph-based approaches

Aman, M. and Said, A.M. and Kadir, S.J.A. and Ullah, I. (2018) Key concept identification: A comprehensive analysis of frequency and topical graph-based approaches. Information (Switzerland), 9 (5).

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Abstract

Automatic key concept extraction from text is the main challenging task in information extraction, information retrieval and digital libraries, ontology learning, and text analysis. The statistical frequency and topical graph-based ranking are the two kinds of potentially powerful and leading unsupervised approaches in this area, devised to address the problem. To utilize the potential of these approaches and improve key concept identification, a comprehensive performance analysis of these approaches on datasets from different domains is needed. The objective of the study presented in this paper is to perform a comprehensive empirical analysis of selected frequency and topical graph-based algorithms for key concept extraction on three different datasets, to identify the major sources of error in these approaches. For experimental analysis, we have selected TF-IDF, KP-Miner and TopicRank. Three major sources of error, i.e., frequency errors, syntactical errors and semantical errors, and the factors that contribute to these errors are identified. Analysis of the results reveals that performance of the selected approaches is significantly degraded by these errors. These findings can help us develop an intelligent solution for key concept extraction in the future. © 2018 by the authors.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Digital libraries; Errors; Graphic methods; Information retrieval; Text processing, Comprehensive analysis; Comprehensive performance; Empirical analysis; Graph-based algorithms; Key concept extractions; Keyphrase extraction; Text mining; Unsupervised approaches, Data mining
Departments / MOR / COE:Research Institutes > Institute for Health Analytics
ID Code:21566
Deposited By: Ahmad Suhairi
Deposited On:01 Aug 2018 03:09
Last Modified:23 Oct 2018 01:48

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