Paper: Unsupervised Word Sense Induction using Distributional Statistics

ACL ID C14-1123
Title Unsupervised Word Sense Induction using Distributional Statistics
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Word sense induction is an unsupervised task to find and characterize different senses of polyse- mous words. This work investigates two unsupervised approaches that focus on using distribu- tional word statistics to cluster the contextual information of the target words using two different algorithms involving latent dirichlet allocation and spectral clustering. Using a large corpus for achieving this task, we quantitatively analyze our clusters on the Semeval-2010 dataset and also perform a qualitative analysis of our induced senses. Our results indicate that our methods suc- cessfully characterized the senses of the target words and were also able to find unconventional senses for those words.