Abstract
Synonymy — different words with the same meaning — is a major problem for text mining systems. We have proposed asymmetric word similarities as a possible solution to this problem, where the similarity between words is computed on the basis of the similarities between contexts in which the words appear, rather than on their syntactic identity. In this paper, we give details of an incremental algorithm to compute word similarities and outline some tests which show the method’s effectiveness.
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Martin, T.P., Azmi-Murad, M. (2005). An Incremental Algorithm to find Asymmetric Word Similarities for Fuzzy Text Mining. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_88
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DOI: https://doi.org/10.1007/3-540-32391-0_88
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