期刊
INFORMATION PROCESSING & MANAGEMENT
卷 54, 期 6, 页码 1002-1021出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2018.07.002
关键词
Semantic similarity; Concept similarity; Information content; Feature-based methods; Wikipedia
资金
- National Natural Science Foundation of China [61772210, 61272066]
- Project of Science and Technology in Guangzhou in China [201807010043]
- key project in universities in Guangdong Province of China [2016KZDXM024]
- Innovation project of postgraduate education in Guangdong Province of China [2016SFKC_13]
Computing Semantic Similarity (SS) between concepts is one of the most critical issues in many domains such as Natural Language Processing and Artificial Intelligence. Over the years, several SS measurement methods have been proposed by exploiting different knowledge resources. Wikipedia provides a large domain-independent encyclopedic repository and a semantic network for computing SS between concepts. Traditional feature-based measures rely on linear combinations of different properties with two main limitations, the insufficient information and the loss of semantic information. In this paper, we propose several hybrid SS measurement approaches by using the Information Content (IC) and features of concepts, which avoid the limitations introduced above. Considering integrating discrete properties into one component, we present two models of semantic representation, called CORM and CARM. Then, we compute SS based on these models and take the IC of categories as a supplement of SS measurement. The evaluation, based on several widely used benchmarks and a benchmark developed by ourselves, sustains the intuitions with respect to human judgments. In summary, our approaches are more efficient in determining SS between concepts and have a better human correlation than previous methods such as Word2Vec and NASARI.
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