Abstract
This paper analyzes the functionality of different distance metrics that can be used in a bottom-up unsupervised algorithm for automatic word categorization. The proposed method uses a modified greedy-type algorithm. The formulations of fuzzy theory are also used to calculate the degree of membership for the elements in the linguistic clusters formed. The unigram and the bigram statistics of a corpus of about two million words are used. Empirical comparisons are made in order to support the discussions proposed for the type of distance metric that would be most suitable for measuring the similarity between linguistic elements.
Cite
CITATION STYLE
Korkmaz, E. E., & Ücoluk, G. (1998). CHOOSING A DISTANCE METRIC FOR AUTOMATIC WORD CATEGORIZATION. In Proceedings of the Joint Conference on New Methods in Language Processing and Computational Natural Language Learning, NeMLaP/CoNLL 1998 (pp. 111–120). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1603899.1603919
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