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Key Concept Identification: A Comprehensive Analysis of Frequency and Topical Graph-Based Approaches

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Department of Computer and Information Sciences, Universiti Teknologi Petronas, Seri Iskander 32610, Malaysia
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Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
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Author to whom correspondence should be addressed.
Information 2018, 9(5), 128; https://doi.org/10.3390/info9050128
Received: 9 February 2018 / Revised: 15 May 2018 / Accepted: 15 May 2018 / Published: 18 May 2018
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. View Full-Text
Keywords: keyphrase extraction; key concept extraction; information retrieval; empirical analysis; text mining keyphrase extraction; key concept extraction; information retrieval; empirical analysis; text mining
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Aman, M.; Bin Md Said, A.; Jadid Abdul Kadir, S.; Ullah, I. Key Concept Identification: A Comprehensive Analysis of Frequency and Topical Graph-Based Approaches. Information 2018, 9, 128.

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