A feature optimization algorithm of concept similarity based on Chinese wikipedia

Xiaofei Chang, Lei Liu*, Mengtao Sun, Yalu Jia, Chunxia Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Concept similarity measure based on feature vector has wide application in various fields, but the problems of polysemy and synonym existing in feature vector affect the similarity measure. We present a feature optimization algorithm based on Chinese Wikipedia which can reduces this effect. First we build a POS feature dictionary (POS-Dic) and a POS Tongyici Cilin(POS-Cilin), and then a new feature vector is used for concept similarity measure. Experiments show that the algorithm effectively reduces the influence of polysemy and synonym on the concept similarity measure.

Original languageEnglish
Title of host publicationICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
EditorsLiang Zhao, Lipo Wang, Guoyong Cai, Kenli Li, Yong Liu, Guoqing Xiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2174-2179
Number of pages6
ISBN (Electronic)9781538621653
DOIs
Publication statusPublished - 21 Jun 2018
Event13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 - Guilin, Guangxi, China
Duration: 29 Jul 201731 Jul 2017

Publication series

NameICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

Conference

Conference13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Country/TerritoryChina
CityGuilin, Guangxi
Period29/07/1731/07/17

Keywords

  • feature vector
  • polysemy
  • similarity
  • synonym

Fingerprint

Dive into the research topics of 'A feature optimization algorithm of concept similarity based on Chinese wikipedia'. Together they form a unique fingerprint.

Cite this