Text Similarity Based on Semantic Analysis

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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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  -