WordNet: a lexical database for English
Communications of the ACM
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Exploiting parallel texts for word sense disambiguation: an empirical study
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Word sense disambiguation vs. statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
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Some of the most used models for statistical word alignment are the IBM models. Although these models generate acceptable alignments, they do not exploit the rich information found in lexical resources, and as such have no reasonable means to choose better translations for specific senses. We try to address this issue by extending the IBM HMM model with an extra hidden layer which represents the senses a word can take, allowing similar words to share similar output distributions. We test a preliminary version of this model on English-French data. We compare different ways of generating senses and assess the quality of the alignments relative to the IBM HMM model, as well as the generated sense probabilities, in order to gauge the usefulness in Word Sense Disambiguation.