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Unsupervised learning of semantic representation for documents with the law of total probability

Published online by Cambridge University Press:  02 November 2017

YANG WEI ,
JINMAO WEI  and
ZHENGLU YANG
YANG WEI
Affiliation:
College of Computer and Control Engineering, Nankai University, Tianjin, China e-mails: wueerfu@hotmail.com, weijm@nankai.edu.cn, yangzl@nankai.edu.cn Institute of Electronics, Chinese Academy of Sciences, Beijing, China
JINMAO WEI
Affiliation:
College of Computer and Control Engineering, Nankai University, Tianjin, China e-mails: wueerfu@hotmail.com, weijm@nankai.edu.cn, yangzl@nankai.edu.cn
ZHENGLU YANG
Affiliation:
College of Computer and Control Engineering, Nankai University, Tianjin, China e-mails: wueerfu@hotmail.com, weijm@nankai.edu.cn, yangzl@nankai.edu.cn
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Abstract

The semantic information of documents needs to be represented because it is the basis for many applications, such as document summarization, web search, and text analysis. Although many studies have explored this problem by enriching document vectors with the relatedness of the words involved, the performance remains far from satisfactory because the physical boundaries of documents hinder the evaluation of the relatedness between words. To address this problem, we propose an effective approach to further infer the implicit relatedness between words via their common related words. To avoid overestimation of the implicit relatedness, we restrict the inference in terms of the marginal probabilities of the words based on the law of total probability. The proposed method measures the relatedness between words, which is confirmed theoretically and experimentally. Thorough evaluation on real datasets illustrates that significant improvement on document clustering has been achieved with the proposed method compared with state-of-the-art methods.

Type
Article
Information
Natural Language Engineering , Volume 24 , Issue 4 , July 2018 , pp. 491 - 522
Copyright
Copyright © Cambridge University Press 2017 

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