Unsupervised Translation Sense Clustering
Mohit Bansal, John DeNero, Dekang Lin · 2012
We propose an unsupervised method for clus-tering the translations of a word, such that the translations in each cluster share a com-mon semantic sense. Words are assigned to clusters based on their usage distribution in large monolingual and parallel corpora using the softK-Means algorithm. In addition to de-scribing our approach, we formalize the task of translation sense clustering and describe a procedure that leverages WordNet for evalu-ation. By comparing our induced clusters to reference clusters generated from WordNet, we demonstrate that our method effectively identifies sense-based translation clusters and benefits from both monolingual and parallel corpora. Finally, we describe a method for an-notating clusters with usage examples. 1