Abstract
This paper presents a new approach to measure the semantic similarity between concepts. By exploiting advantages of distance (edge-base) approach for taxonomic tree-like concepts, we enhance the strength of information theoretic (node-based) approach. Our measure therefore gives a complete view of word similarity, which cannot be achieved by solely applying node-based approach. Our experimental measure achieves 88% correlation with human rating.
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Hong-Minh, T., Smith, D. (2008). Word Similarity In WordNet. In: Bock, H.G., Kostina, E., Phu, H.X., Rannacher, R. (eds) Modeling, Simulation and Optimization of Complex Processes. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79409-7_19
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DOI: https://doi.org/10.1007/978-3-540-79409-7_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79408-0
Online ISBN: 978-3-540-79409-7
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