Distributional Features for Text Categorization

Abstract

In previous research of text categorization, a word is usually described by features which express that whether the word appears in the document or how frequently the word appears. Although these features are useful, they have not fully expressed the information contained in the document. In this paper, the distributional features are used to describe a word, which express the distribution of a word in a document. In detail, the compactness of the appearances of the word and the position of the first appearance of the word are characterized as features. These features are exploited by a TFIDF style equation in this paper. Experiments show that the distributional features are useful for text categorization. In contrast to using the traditional term frequency features solely, including the distributional features requires only a little additional cost, while the categorization performance can be significantly improved.