TY - CONF T1 - PUTOP: turning predominant senses into a topic model for word sense disambiguation T2 - Proceedings of the 4th International Workshop on Semantic Evaluations Y1 - 2007 A1 - Jordan Boyd-Graber A1 - Blei,David AB - We extend on McCarthy et al.'s predominant sense method to create an unsupervised method of word sense disambiguation that uses automatically derived topics using Latent Dirichlet allocation. Using topic-specific synset similarity measures, we create predictions for each word in each document using only word frequency information. It is hoped that this procedure can improve upon the method for larger numbers of topics by providing more relevant training corpora for the individual topics. This method is evaluated on SemEval-2007 Task 1 and Task 17. JA - Proceedings of the 4th International Workshop on Semantic Evaluations T3 - SemEval '07 PB - Association for Computational Linguistics CY - Stroudsburg, PA, USA UR - http://dl.acm.org/citation.cfm?id=1621474.1621534 ER -