Abstract
The estimation of semantic similarity between words play an important role in many language related applications. In this paper, we survey most of the ontology-based approaches in order to evaluate their advantages and limitations. We also present an approach for measuring semantic similarity. As a kind of feature-based method, proposed method extracts taxonomic features from ontology, aiming to provide a high-efficient, simple and reliable semantic similarity assessment method. We evaluate and compare our approach’s results against those reported by related works under a common framework. Result demonstrated that the proposed method has higher correlation with human subjective judgment than most of existing methods.
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Acknowledgment
This paper is supported by the National Natural Science Funds of China (61272015, 61050004), and also is supported by Henan Province basic and frontier technology research project (142300410303).
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Zhang, R., Xiong, S., Chen, Z. (2015). An Ontology-Based Approach for Measuring Semantic Similarity Between Words. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_54
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DOI: https://doi.org/10.1007/978-3-319-22053-6_54
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