IEICE TRANSACTIONS on Information
Measuring Semantic Similarity between Words Based on Multiple Relational Information
Summary :
The similarity of words extracted from the rich text relation network is the main way to calculate the semantic similarity. Complex relational information and text content in Wikipedia website, Community Question Answering and social network, provide abundant corpus for semantic similarity calculation. However, most typical research only focused on single relationship. In this paper, we propose a semantic similarity calculation model which integrates multiple relational information, and map multiple relationship to the same semantic space through learning representing matrix and semantic matrix to improve the accuracy of semantic similarity calculation. In experiments, we confirm that the semantic calculation method which integrates many kinds of relationships can improve the accuracy of semantic calculation, compared with other semantic calculation methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.1 pp.163-169
- Publication Date
- 2020/01/01
- Publicized
- 2019/09/27
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7083
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
Jianyong DUAN
North China University of Technology,Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data
Yuwei WU
North China University of Technology,Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data
Mingli WU
North China University of Technology,Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data
Hao WANG
North China University of Technology,Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data
Keyword
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Jianyong DUAN, Yuwei WU, Mingli WU, Hao WANG, "Measuring Semantic Similarity between Words Based on Multiple Relational Information" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 1, pp. 163-169, January 2020, doi: 10.1587/transinf.2019EDP7083.
Abstract: The similarity of words extracted from the rich text relation network is the main way to calculate the semantic similarity. Complex relational information and text content in Wikipedia website, Community Question Answering and social network, provide abundant corpus for semantic similarity calculation. However, most typical research only focused on single relationship. In this paper, we propose a semantic similarity calculation model which integrates multiple relational information, and map multiple relationship to the same semantic space through learning representing matrix and semantic matrix to improve the accuracy of semantic similarity calculation. In experiments, we confirm that the semantic calculation method which integrates many kinds of relationships can improve the accuracy of semantic calculation, compared with other semantic calculation methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7083/_p
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@ARTICLE{e103-d_1_163,
author={Jianyong DUAN, Yuwei WU, Mingli WU, Hao WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Measuring Semantic Similarity between Words Based on Multiple Relational Information},
year={2020},
volume={E103-D},
number={1},
pages={163-169},
abstract={The similarity of words extracted from the rich text relation network is the main way to calculate the semantic similarity. Complex relational information and text content in Wikipedia website, Community Question Answering and social network, provide abundant corpus for semantic similarity calculation. However, most typical research only focused on single relationship. In this paper, we propose a semantic similarity calculation model which integrates multiple relational information, and map multiple relationship to the same semantic space through learning representing matrix and semantic matrix to improve the accuracy of semantic similarity calculation. In experiments, we confirm that the semantic calculation method which integrates many kinds of relationships can improve the accuracy of semantic calculation, compared with other semantic calculation methods.},
keywords={},
doi={10.1587/transinf.2019EDP7083},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Measuring Semantic Similarity between Words Based on Multiple Relational Information
T2 - IEICE TRANSACTIONS on Information
SP - 163
EP - 169
AU - Jianyong DUAN
AU - Yuwei WU
AU - Mingli WU
AU - Hao WANG
PY - 2020
DO - 10.1587/transinf.2019EDP7083
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2020
AB - The similarity of words extracted from the rich text relation network is the main way to calculate the semantic similarity. Complex relational information and text content in Wikipedia website, Community Question Answering and social network, provide abundant corpus for semantic similarity calculation. However, most typical research only focused on single relationship. In this paper, we propose a semantic similarity calculation model which integrates multiple relational information, and map multiple relationship to the same semantic space through learning representing matrix and semantic matrix to improve the accuracy of semantic similarity calculation. In experiments, we confirm that the semantic calculation method which integrates many kinds of relationships can improve the accuracy of semantic calculation, compared with other semantic calculation methods.
ER -
English
