Efficient and density adaptive edge weight model for measuring semantic similarity
Authors: 
Fei Li, Lejian Liao, Chunyi Li, Sixing HeAuthors Info & Claims
Fei Li, Lejian Liao, Chunyi Li, Sixing HeAuthors Info & ClaimsPages 127 - 134
Abstract
The measurement of semantic similarity between concepts is an important research topic in natural language processing. However, previous efforts suffered from the mismatch of the accuracy and efficiency. In this paper, we propose an edge weight model for improving the accuracy of edge-based measures that have an inherent high efficiency. It combines the edge counting model with the information theory and deduces a function of edge weight based on the number of direct hyponyms of the subsumer in the edge. This model doesn't require any additional parameter and can adapt the effect of different densities to edges. Extensive experiments on four test datasets for WordNet and SNOMED-CT demonstrate that the proposed edge weight model can significantly improve the accuracy of various edge-based similarity measures and has a wide coverage over different ontologies. Compared with IC-based measures, our model has a remarkable advantage in efficiency and is comparable to it in accuracy.
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- Efficient and density adaptive edge weight model for measuring semantic similarity
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Information & Contributors
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Published In
November 2018
326 pages
ISBN:9781450365345
DOI:10.1145/3290420
- Conference Chairs:
- Jalel Ben-Othman,
- Hui Yu,
- Program Chairs:
- Herwig Unger,
- Masayuki Arai
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Published: 02 November 2018
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- the National Natural Science Foundation of China
- Graduate Technological Innovation Project of Beijing Institute of Technology
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ICCIP 2018
ICCIP 2018: 2018 the 4th International Conference on Communication and Information Processing
November 2 - 4, 2018
Qingdao, China
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Overall Acceptance Rate 61 of 301 submissions, 20%
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