Text classification using concept kernel

摘要

Texts may be classified by mapping the texts to concept space, and by dividing the concept space based on substantive classes. A concept space containing a diverse set of concepts is defined. One example of a concept space is the set of on-line encyclopedia articles, each of which is an example of a concept. A text is scored for relevance against each concept, and a vector is created containing each of the scores. The vector represents the text's position in concept space. For any given substantive class of texts, the concept space may be divided into regions containing texts that are members/non-members of the class. The dividing boundary may be determined by training a classifier on a set of labeled examples of texts that fall inside and outside the class.