Category hierarchy maintenance : a data-driven approach

Abstract

Category hierarchies often evolve at a much slower pace than the documents reside in. With newly available documents kept adding into a hierarchy, new topics emerge and documents within the same category become less topically cohesive. In this paper, we propose a novel automatic approach to modifying a given category hierarchy by redistributing its documents into more topically cohesive categories. The modification is achieved with three operations (namely, sprout, merge, and assign) with reference to an auxiliary hierarchy for additional semantic information; the auxiliary hierarchy covers a similar set of topics as the hierarchy to be modified. Our user study shows that the modified category hierarchy is semantically meaningful. As an extrinsic evaluation, we conduct experiments on document classification using real data from Yahoo! Answers and AnswerBag hierarchies, and compare the classification accuracies obtained on the original and the modified hierarchies. Our experiments show that the proposed method achieves much larger classification accuracy improvement compared with several baseline methods for hierarchy modification.