Word Similarity Measurement Based on Concept Primitive

CHI Zhejie; ZHANG Quan

doi:10.11999/JEIT160176

Volume 39 Issue 1

Jan. 2017

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CHI Zhejie, ZHANG Quan. Word Similarity Measurement Based on Concept Primitive[J]. Journal of Electronics & Information Technology, 2017, 39(1): 150-158. doi: 10.11999/JEIT160176

Citation:

CHI Zhejie, ZHANG Quan. Word Similarity Measurement Based on Concept Primitive[J]. Journal of Electronics & Information Technology, 2017, 39(1): 150-158. doi: 10.11999/JEIT160176

CHI Zhejie, ZHANG Quan. Word Similarity Measurement Based on Concept Primitive[J]. Journal of Electronics & Information Technology, 2017, 39(1): 150-158. doi: 10.11999/JEIT160176

Citation:

CHI Zhejie, ZHANG Quan. Word Similarity Measurement Based on Concept Primitive[J]. Journal of Electronics & Information Technology, 2017, 39(1): 150-158. doi: 10.11999/JEIT160176

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Word Similarity Measurement Based on Concept Primitive

doi: 10.11999/JEIT160176

CHI Zhejie^{1
,
,},
ZHANG Quan¹

Funds:

The Twelfth Five-Year Project of National 863 Program of China (2012AA011102), The State Language Commission Twelfth Five-Year Research Project (YB125-53)

Received Date: 2016-02-25
Rev Recd Date: 2016-09-14
Publish Date: 2017-01-19

Abstract

Abstract

Word similarity measurement plays an important role in machine learning, information retrieval and many other fields. Regarding the concept primitive symbol system of Hierarchical network of concepts theory as semantic resource and comparing commonness with difference, a multi-dimensional computational method for similarity is proposed which considers the hierarchy, netted nature, comparability and duality, attached feature and quintuple information of the system. Weight strategy is introduced for node depth and distance measurement to increase the discrimination of node level. Experiment on manual scoring test set shows that the computed similarities are consistent with human judgments. The proposed method achieves 0.812, 0.786, and 0.775 in compatibility degree, correlation coefficient, and ordinal pair conformity respectively. Meanwhile, the result of correlation test further proofs that the computed similarities and humans scores are significantly correlated.
- Word similarity,
- Semantic distance,
- Hierarchical network of concepts,
- Concept primitive

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References(29)

References

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