doi: 10.4304/jsw.9.9.2461-2466
Semantic Similarity Computation Based on Multi-feature Combination using HowNet
- 1 China University of Petroleum (East China)/ College of computer and communication engineering, QingDao, China
- 2 Tongji University / International School, Shanghai, China
Abstract
Semantic similarity between words is becoming a generic problems for many applications of computational linguistics and artificial intelligence. The difficulty lies in how to develop a computational method that is capable of generating satisfactory results close to how humans perceive. This paper proposes a semantic similarity approach that is based on multi-feature combination. One of the benchmarks is Miller and Charles’ list of 30 noun pairs which had been manually designated similarity measurements. We correlate our experiments with those computed by several other methods. Experiments on Chinese word pairs show that our approach is close to human similarity judgments.
Keywords
- semantic similarity
- semantic distance
How to Cite
Peiying Zhang, Zhanshan Zhang, Weishan Zhang, and Chunlei Wu, "Semantic Similarity Computation Based on Multi-feature Combination using HowNet," Journal of Software, vol. 9, no. 9, pp. 2461-2466, 2014. https://doi.org/ 10.4304/jsw.9.9.2461-2466
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).