Mining for context recognition in document filtering and classification

Abstract

Much information has been hierarchically organized to facilitate information browsing, retrieval, and dissemination. In practice, much information may be entered at any time, but only a small subset of the information may be classified into some categories in a hierarchy. Therefore, achieving document filtering (DF) in the course of document classification (DC) is an essential basis to develop an information center, which classifies suitable documents into suitable categories, reducing information overload while facilitating information sharing. In this paper, we present a technique ICenter, which conducts DF and DC by recognizing the context of discussion (COD) of each document and category. Experiments on realworld data show that, through COD recognition, the performance of ICenter is significantly better. The results are of theoretical and practical significance. ICenter may serve as an essential basis to develop an information center for a user community, which shares and organizes a hierarchy of textual information.