Paper: Models and Training for Unsupervised Preposition Sense Disambiguation

ACL ID P11-2056
Title Models and Training for Unsupervised Preposition Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We present a preliminary study on unsu- pervised preposition sense disambiguation (PSD), comparing different models and train- ing techniques (EM, MAP-EM with L0 norm, Bayesian inference using Gibbs sampling). To our knowledge, this is the first attempt at un- supervised preposition sense disambiguation. Our best accuracy reaches 56%, a significant improvement (at p <.001) of 16% over the most-frequent-sense baseline.