In this paper we adopted Latent Semantic Kernels to perform a Term Categorization task, and we applied this technique to assign domain labels to monosemous words. Results show that the proposed technique is effective, achieving an accuracy of about 43\% for all the monosemus terms in a corpus. We also reported an error analysis showing that most of the misclassification errors are related the the fuzzy nature of domain distinctions. In particular we identified a set of "families" in the \wordnetd\ categories that makes difficult the classification task
Automatic Acquisition of Domain Information for Lexical Concepts
D'Avanzo, Ernesto;Gliozzo, Alfio Massimiliano;Strapparava, Carlo
2005-01-01
Abstract
In this paper we adopted Latent Semantic Kernels to perform a Term Categorization task, and we applied this technique to assign domain labels to monosemous words. Results show that the proposed technique is effective, achieving an accuracy of about 43\% for all the monosemus terms in a corpus. We also reported an error analysis showing that most of the misclassification errors are related the the fuzzy nature of domain distinctions. In particular we identified a set of "families" in the \wordnetd\ categories that makes difficult the classification taskFile in questo prodotto:
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