Abstract
Semantic relatedness computation is the task of quantifying the degree of relatedness of two concepts. The performance of existing approaches to computing semantic relatedness is highly dependent on particular aspects of relatedness. For instance, taxonomy-based approaches aim at computing similarity, which is a special case of semantic relatedness. On the other hand, corpus-based approaches focus on the associative relations of words by taking their distributional features into account. Based on the assumption that different aspects of knowledge sources cover different kinds of semantic relations, this paper presents a hybrid model for computing semantic relatedness of words using new features extracted from various aspects of Wikipedia. The focus of this paper is on finding the optimal feature combination(s) that enhance the performance of the hybrid model. The empirical evaluation on benchmark datasets has shown that hybrid features perform better than single features by providing a complementary coverage of semantic relations, leading to improved correlation with human judgments.
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Jabeen, S., Gao, X., Andreae, P. (2014). A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_39
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DOI: https://doi.org/10.1007/978-3-319-11749-2_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11748-5
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