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A Bayesian Approach to Learning the Structure of Human Languages

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Grammar Induction has long been a central challenge of Computational Linguistics. Empirically demonstrating the ability of computational models to automatically learn the syntactic structure of human languages will impact upon both our understanding of how children learn language, and our ability to build sophisticated language technologies. In this talk I will describe our recently developed state-of-the-art approach to syntax induction. Using hierarchical non-parametric Bayesian priors we have created probabilistic models of syntactic part-of-speech and dependency grammar that are able to integrate information across a range of granularities. The promising results achieved by these models indicate that the great challenge of Grammar Induction may not be as intractable as long thought.

This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

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