International Journal of Computational Intelligence Systems

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Volume 10, Issue 1, 2017, Pages 593 - 604

SAO Semantic Information Identification for Text Mining

Authors
Chao Yangyc_2009@hotmail.com, Donghua Zhuzhudh111@bit.edu.cn, Xuefeng Wang*, wxf5122@bit.edu.cn
School of Management and Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
*Corresponding author.
Corresponding Author
Xuefeng Wangwxf5122@bit.edu.cn
Received 28 November 2016, Accepted 22 December 2016, Available Online 1 January 2017.
DOI
10.2991/ijcis.2017.10.1.40How to use a DOI?
Keywords
Semantic Analysis; Technology Intelligence; Computational Intelligence; Topic Model; Subject-Action-Object
Abstract

A Subject-Action-Object (SAO) is a triple structure which can be used to both describe topics in detail and explore the relationship between them. SAO analysis has become popular in bibliometrics, however there are two challenges in the identification of SAO structures: low relevance of SAOs to domain topics; and synonyms in SAO. These problems make the identification of SAO greatly dependent upon domain experts, limiting the further usage of SAO and influencing further the mining of SAO characteristics. This paper proposes a parse tree-based SAO identification method that includes (1) a model to identify the core components (candidate terms for subject & object) of SAO structures, where term clumping processes and co-word analysis are involved; (2) a parse tree-based hierarchical SAO extraction model to divide entire SAO structures into a collection of simpler sub-tasks for separate subject, action, and object identification; and (3) an SAO weighting model to rank SAO structures for result selection. The proposed method is applied to publications in the Journal of Scientometrics (SCIM), to identify and rank significant SAO structures. Our experiment results demonstrate the validity and feasibility of the proposed method.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
10 - 1
Pages
593 - 604
Publication Date
2017/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2017.10.1.40How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Chao Yang
AU  - Donghua Zhu
AU  - Xuefeng Wang
PY  - 2017
DA  - 2017/01/01
TI  - SAO Semantic Information Identification for Text Mining
JO  - International Journal of Computational Intelligence Systems
SP  - 593
EP  - 604
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.40
DO  - 10.2991/ijcis.2017.10.1.40
ID  - Yang2017
ER  -