Automatic Classification of Documents by Random Sampling
نویسندگان
چکیده
This paper presents a thesaurus-based approach to document classification. We define a classification space based on the notion of theme vectors. For a new text, we compute its characteristic vector by considering only a sample of randomly extracted lemmas. Then, we compute the differences between this vector and the vectors in the document model and the classification of the new text is decided based on the closest vector. We introduce a family of document classifiers depending on a parameter, and present a statistical procedure to evaluate their effectiveness for different sized corpora. We show that they have statistically distinct behavior so that it makes sense to look for an optimal value of the classifier parameter. We suggest that our method can also be used in comparing different ontologies with respect to their support in document classification and that the same method can be used in assessing corpora homogeneity.
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