Taxonomy Learning Using Word Sense Induction

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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, developing taxonomies that conflate word senses. In this paper, we present an unsupervised method that builds a taxonomy of senses learned automatically from an unlabelled corpus. 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 traditional distributional similarity approaches that merge senses by grouping the features of each word into a single vector.
Original languageEnglish
Title of host publicationHuman Language Technologies
Subtitle of host publicationThe 2010 Annual Conference of the North American Chapter of the ACL
PublisherAssociation for Computational Linguistics
Publication statusPublished - 2010

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