Using noun phrases extraction for the improvement of hybrid clustering with text- and citation-based components. The example of “Information Systems Research”

Thijs, Bart, Glänzel, Wolfgang, Meyer, Martin S. (2015) Using noun phrases extraction for the improvement of hybrid clustering with text- and citation-based components. The example of “Information Systems Research”. In: Proc. of the Workshop Mining Scientific Papers: Computational Linguistics and Bibliometrics, 15th International Society of Scientometrics and Informetrics Conference (ISSI), Istanbul, Turkey. 1384. pp. 28-33.

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Abstract

The hybrid clustering approach combining lexical and link-based similarities suffered for a long time from the different properties of the underlying networks. We propose a method based on noun phrase extraction using natural language processing to improve the measurement of the lexical component. Term shingles of different length are created form each of the extracted noun phrases. Hybrid networks are built based on weighted combination of the two types of similarities with seven different weights. We conclude that removing all single term shingles provides the best results at the level of computational feasibility, comparability with bibliographic coupling and also in a community detection application.

Item Type: Conference or workshop item (Paper)
Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: Martin Meyer
Date Deposited: 19 Mar 2016 07:28 UTC
Last Modified: 29 May 2019 17:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/54572 (The current URI for this page, for reference purposes)
Meyer, Martin S.: https://orcid.org/0000-0002-5598-9480
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