Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus

Taxonomies hierarchically organize concepts in a domain. Building and maintaining them by hand is a tedious and time-consuming task. This paper proposes a novel, unsupervised algorithm for auto-matically learning an IS-A taxonomy from scratch by analyzing a given text corpus. Our approach is designed to deal with infrequently occurring con-cepts, so it can effectively induce taxonomies even from small corpora. Algorithmically, the approach makes two important contributions. First, it per-forms inference based on clustering and the distri-butional semantics, which can capture links among concepts never mentioned together. Second, it uses a novel graph-based algorithm to detect and remove incorrect is-a relations from a taxonomy. An em-pirical evaluation on five corpora demonstrates the utility of our proposed approach.