Bayesian Approach

We describe, analyze, and experimentally evaluate a new probabilistic model for word- sequence prediction in natural languages, based on prediction suffi~v trees (PSTs). By using efficient data structures, we extend …...

Unsupervised learning of linguistic structure is a difficult problem. A common approach is to define a generative model and max- imize the probability of the hidden struc- ture given the …...

We present an unsupervised, nonparamet- ric Bayesian approach to coreference reso- lution which models both global entity iden- tity across a corpus as well as the sequen- tial anaphoric structure …...

This paper investigates why the HMMs es- timated by Expectation-Maximization (EM) produce such poor results as Part-of-Speech (POS) taggers. We find that the HMMs es- timated by EM generally assign …...

This paper describes a novel Bayesian ap- proach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of well- formed segments to induce …...

We introduce a novel Bayesian approach for deciphering complex substitution ciphers. Our method uses a decipherment model which combines information from letter n-gram lan- guage models as well as word …...

In previous work on unsupervised learning of morphology, the long-tail pattern in the rank-frequency distribution of words, as well as of morphological units, is usually considered as following Zipf’s law …...