Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

Text Similarity Based on Semantic Analysis

Authors
Junli Wang, Qing Zhou, Guobao Sun
Corresponding Author
Junli Wang
Available Online November 2016.
DOI
10.2991/aiie-16.2016.70How to use a DOI?
Keywords
semantic similarity; bayesian network; graph model
Abstract

One of the most important challenges in measuring text similarity is language variability: texts with the same meaning can be realized in several ways. A way to address the language variability is the notion of semantic similarity. This paper extracts the relevance of texts and terms through Singular Value Decomposition (SVD). According to Bayesian Network, we construct term-topic sets and then use Mutual Information (MI) to calculate the semantic similarity between terms. Finally, we use graph structures instead of term vectors to calculate text similarity.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-271-8
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.70How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Junli Wang
AU  - Qing Zhou
AU  - Guobao Sun
PY  - 2016/11
DA  - 2016/11
TI  - Text Similarity Based on Semantic Analysis
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 303
EP  - 307
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.70
DO  - 10.2991/aiie-16.2016.70
ID  - Wang2016/11
ER  -