By the same authors

Taxonomy Learning Using Word Sense Induction

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



Publication details

Title of host publicationHuman Language Technologies
DatePublished - 2010
PublisherAssociation for Computational Linguistics
Original languageEnglish


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.

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