Taxonomy Learning Using Word Sense Induction

TY - GEN

T1 - Taxonomy Learning Using Word Sense Induction

AU - Klapaftis, Ioannis P.

AU - Manandhar, Suresh

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84858421418&partnerID=8YFLogxK

M3 - Conference contribution

SP - 82

EP - 90

BT - Human Language Technologies

PB - Association for Computational Linguistics

ER -