An association network for computing semantic relatedness

Keyang Zhang, Kenny Q. Zhu, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

To judge how much a pair of words (or texts) are semanti-cally related is a cognitive process. However, previous algorithms for computing semantic relatedness are largely based on co-occurrences within textual windows, and do not actively leverage cognitive human perceptions of relatedness. To bridge this perceptional gap, we propose to utilize free association as signals to capture such human perceptions. However, free association, being manually evaluated, has limited lexical coverage and is inherently sparse. We propose to expand lexical coverage and overcome sparseness by constructing an association network of terms and concepts that combines signals from free association norms and five types of cooccurrences extracted from the rich structures of Wikipedia. Our evaluation results validate that simple algorithms on this network give competitive results in computing semantic relatedness between words and between short texts.

Original languageEnglish
Title of host publicationProceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PublisherAI Access Foundation
Pages593-599
Number of pages7
ISBN (Electronic)9781577356998
Publication statusPublished - 2015 Jun 1
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
Duration: 2015 Jan 252015 Jan 30

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
Country/TerritoryUnited States
CityAustin
Period15/1/2515/1/30

Bibliographical note

Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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