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Keyword Extraction Based on Selectivity and Generalized Selectivity

Keyword Extraction Based on Selectivity and Generalized Selectivity

Slobodan Beliga, Ana Meštrović, Sanda Martinčić-Ipšić
Copyright: © 2018 |Pages: 35
ISBN13: 9781522550426|ISBN10: 1522550429|EISBN13: 9781522550433
DOI: 10.4018/978-1-5225-5042-6.ch007
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MLA

Beliga, Slobodan, et al. "Keyword Extraction Based on Selectivity and Generalized Selectivity." Innovations, Developments, and Applications of Semantic Web and Information Systems, edited by Miltiadis D. Lytras, et al., IGI Global, 2018, pp. 170-204. https://doi.org/10.4018/978-1-5225-5042-6.ch007

APA

Beliga, S., Meštrović, A., & Martinčić-Ipšić, S. (2018). Keyword Extraction Based on Selectivity and Generalized Selectivity. In M. Lytras, N. Aljohani, E. Damiani, & K. Chui (Eds.), Innovations, Developments, and Applications of Semantic Web and Information Systems (pp. 170-204). IGI Global. https://doi.org/10.4018/978-1-5225-5042-6.ch007

Chicago

Beliga, Slobodan, Ana Meštrović, and Sanda Martinčić-Ipšić. "Keyword Extraction Based on Selectivity and Generalized Selectivity." In Innovations, Developments, and Applications of Semantic Web and Information Systems, edited by Miltiadis D. Lytras, et al., 170-204. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5042-6.ch007

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Abstract

This chapter presents a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction.

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