Paper: Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models

ACL ID P04-1037
Title Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

We describe two probabilistic models for unsuper- vised word-sense disambiguation using parallel cor- pora. The rst model, which we call the Sense model, builds on the work of Diab and Resnik (2002) that uses both parallel text and a sense in- ventory for the target language, and recasts their ap- proach in a probabilistic framework. The second model, which we call the Concept model, is a hier- archical model that uses a concept latent variable to relate different language speci c sense labels. We show that both models improve performance on the word sense disambiguation task over previous unsu- pervised approaches, with the Concept model show- ing the largest improvement. Furthermore, in learn- ing the Concept model, as a by-product, we learn a sense inventory for the parallel language.