A Comparison of Concept-base Model and Word Distributed Model as Word Association System | Zendy

Akihiro Toyoshima | Zendy; Noriyuki Okumura | Zendy

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A Comparison of Concept-base Model and Word Distributed Model as Word Association System

Author(s) -

Akihiro Toyoshima,

Noriyuki Okumura

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.08.080

Subject(s) - word2vec , computer science , associative property , word (group theory) , artificial intelligence , natural language processing , association (psychology) , table (database) , basis (linear algebra) , base (topology) , word association , construct (python library) , data mining , linguistics , mathematics , mathematical analysis , philosophy , geometry , embedding , epistemology , pure mathematics , programming language

We construct Concept-base based on concept chain model and word vector spaces based on Word2Vec using EDR-electronic- dictionary and Japanese Wikipedia data. This paper describes verification experiments of these models regarding the word association system based on the association-frequency-table. In these experiments, we investigate the tendency using associative words of evaluation basis words obtained by these models. In Concept-base model, we observed a tendency that synonyms, superordinate words, and subordinate words are obtained as associative words. Furthermore we observed a tendency that words, which can be compounds or co-occurrence phrases after connecting headwords of the association-frequency-table, are used as associative words in the Word2Vec model. Moreover evaluation result showed the tendency that associative words mostly have category words in the Word2Vec model

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