Paper: Taxonomy Learning Using Word Sense Induction

ACL ID N10-1010
Title Taxonomy Learning Using Word Sense Induction
Venue Human Language Technologies
Session Main Conference
Year 2010

Taxonomies are an important resource for a variety of Natural Language Processing (NLP) applications. Despite this, the current state- of-the-art methods in taxonomy learning have disregarded word polysemy, in effect, devel- oping taxonomies that conflate word senses. In this paper, we present an unsupervised method that builds a taxonomy of senses learned automatically from an unlabelled cor- pus. Our evaluation on two WordNet-derived taxonomies shows that the learned taxonomies capture a higher number of correct taxonomic relations compared to those produced by tradi- tional distributional similarity approaches that merge senses by grouping the features of each word into a single vector.