Clustering Document with Active Learning using Wikipedia

Wikipedia has been applied as a background knowledge base to various text mining problems, including document categorization, topic indexing and information extraction. However, very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit Wikipedia and the semantic knowledge therein to facilitate clustering, enabling the automatic grouping of documents with similar themes. Although clustering is intrinsically unsupervised, recent research has shown that incorporating supervision improves clustering performance, even when limited supervision is provided. The approach presented in this paper applies supervision using active learning. We first utilize Wikipedia to create a concept-based representation of a text document, with each concept associated to a Wikipedia article. We then exploit the semantic relatedness between Wikipedia concepts to find pair-wise instancelevel constraints for supervising clustering, guiding clustering towards the direction indicated by the constraints. We test our approach on three standard text document datasets. Empirical results show that our basic document representation strategy yields comparable performance to previous attempts. Adding constraints improves clustering performance further by up to 20%.