Identifying influential segments from word co-occurrence networks using AHP-Reference-Cited by-同舟云学术

Identifying influential segments from word co-occurrence networks using AHP

Published:2018-01 Issue: Volume:47 Page:28-41
ISSN:1389-0417
Container-title:Cognitive Systems Research
language:en
Short-container-title:Cognitive Systems Research

Author:

Garg Muskan,Kumar Mukesh

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Experimental and Cognitive Psychology,Software

Reference21 articles.

1. A keyword extraction method from twitter messages represented as graphs;Abilhoa;Applied Mathematics and Computation,2014

2. Sensing trending topics in Twitter;Aiello;IEEE Transactions on Multimedia,2013

3. An overview of graph-based keyword extraction methods and approaches;Beliga;Journal of Information and Organizational Sciences,2015

4. Semantic networks: Structure and dynamics;Borge-Holthoefer;Entropy,2010

5. Boudin, F. (2013, October). A comparison of centrality measures for graph-based keyphrase extraction. In International joint conference on natural language processing (IJCNLP) (pp. 834–838).

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