Location of AV Williams


We learn how the words of a language are inflected, given a plain text corpus plus a small supervised set of known paradigms. The approach is principled, simply performing empirical Bayesian inference under a straightforward generative model that explicitly describes the generation of:

1. The grammar and subregularities of the language (via many

finite-state transducers coordinated in a Markov Random Field).

2. The infinite inventory of types and their inflectional paradigms

(via a Dirichlet Process Mixture Model based on the above grammar).

3. The corpus of tokens (by sampling inflected words from the above


Our inference algorithm cleanly integrates several techniques that handle the different levels of the model: classical dynamic programming operations on the finite-state transducers, loopy belief propagation in the Markov Random Field, and MCMC and MCEM for the non-parametric Dirichlet Process Mixture Model.

We will build up the various components of the model in turn, showing experimental results along the way for several intermediate tasks such as lemmatization, transliteration, and inflection. Finally, we show that modeling paradigms jointly with the Markov Random Field, and learning from unannotated text corpora via the non-parametric model, significantly improves the quality of predicted word inflections. This is joint work with Markus Dreyer.

Bio: Jason Eisner is Associate Professor of Computer Science at Johns Hopkins University, where he is also affiliated with the Center for Language and Speech Processing, the Cognitive Science Department, and the national Center of Excellence in Human Language Technology. He is particularly interested in designing algorithms that statistically exploit linguistic structure. His 80 or so papers have presented a number of algorithms for parsing and machine translation; algorithms for constructing and training weighted finite-state machines; formalizations, algorithms, theorems and empirical results in computational phonology; and unsupervised or semi-supervised learning methods for domains such as syntax, morphology, and word-sense disambiguation.