Models and Training for Unsupervised Preposition Sense Disambiguation

We present a preliminary study on unsu-pervised preposition sense disambiguation (PSD), comparing different models and training 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.

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