KSU KDD: Word Sense Induction by Clustering in Topic Space
Computation and Language
2015-03-06 v1 Artificial Intelligence
Applications
Machine Learning
Abstract
We describe our language-independent unsupervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allocation (LDA) topic model then uses it to infer the topics distribution of the test instances. By clustering these topics distributions in their topic space we cluster them into different senses. Our hypothesis is that closeness in topic space reflects similarity between different word senses. This system participated in SemEval-2 word sense induction and disambiguation task and achieved the second highest V-measure score among all other systems.
Cite
@article{arxiv.1302.7056,
title = {KSU KDD: Word Sense Induction by Clustering in Topic Space},
author = {Wesam Elshamy and Doina Caragea and William Hsu},
journal= {arXiv preprint arXiv:1302.7056},
year = {2015}
}