Transductive Learning from Textual Data with Relevant Example Selection

In many textual repositories, documents are organized in a hierarchy of categories to support a thematic search by browsing topics of interests. In this paper we present a novel approach for automatic classification of documents into a hierarchy of categories that works in the transductive setting and exploits relevant example selection. While resorting to the transductive learning setting permits to classify repositories where only few examples are labelled by exploiting information potentially conveyed by unlabelled data, relevant example selection permits to tame the complexity of the task and increase the rate of learning by focusing only on informative examples. Results on real world datasets show the effectiveness of the proposed solutions.