[Ml-stat-talks] ML lectures: David Smith on inference and parsing

Christiane Fellbaum fellbaum at Princeton.EDU
Tue Feb 15 16:50:09 EST 2011


Yes, and (most importantly?) Watson needs to get the context-appropriate 
meaning of polysemous words.
He heavily relies on  (tadah!) WordNet for this....

Christiane


David Mimno wrote:
> Have you been watching Watson compete on Jeopardy? To answer natural
> language questions, computers need to understand syntax, and to understand
> syntax, they need parsers. Next week we'll be inaugurating our 
> Google-sponsored Machine Learning lecture series with David Smith,
> who will discuss new approaches to the problem of training parsers 
> from data -- sometimes very little data. [-DM]
>
> (For upcoming talks, see http://www.cs.princeton.edu/~mimno/mltalks.html)
>
> WHEN: Mon Feb 21, 3:00PM
> WHERE: CS 402
>
> David Smith (UMass, Amherst)
>
> Title: Efficient Inference for Declarative Approaches to Language
>
> Abstract:
> Much recent work in natural language processing treats linguistic
> analysis as an inference problem over graphs. This development opens
> up useful connections between machine learning, graph theory, and
> linguistics.
>
> The first part of this talk formulates syntactic dependency parsing as
> a dynamic Markov random field with the novel ingredient of global
> constraints. Global constraints are propagated by combinatorial
> optimization algorithms, which greatly improve on collections of local
> constraints. In particular, such factors enforce the constraint that
> the parser's output variables must form a tree. Even with second-order
> features or latent variables, which would make exact parsing
> asymptotically slower or NP-hard, accurate approximate inference with
> belief propagation is as efficient as a simple edge-factored parser
> times a constant factor. Inference can be further sped up by ignoring
> 98% of the higher-order factors that do not contribute significantly
> to overall accuracy.
>
> The second part extends these models to capture correspondences among
> non-isomorphic structures. When bootstrapping a parser in a
> low-resource target language by exploiting a parser in a high-resource
> source language, models that score the alignment and the
> correspondence of divergent syntactic configurations in translational
> sentence pairs achieve higher accuracy in parsing the target language.
> These noisy (quasi-synchronous) mappings have further applications in
> adapting parsers across domains, in learning features of the
> syntax-semantics interface, and in question answering, paraphrasing,
> and information retrieval.
>
> Bio:
>
> David Smith is a Research Assistant Professor in the Computer Science
> Department of the University of Massachusetts, Amherst, where he is
> affiliated with the Center for Intelligent Information Retrieval. He
> conducts research in inference and learning of phonology, morphology,
> syntax, and semantics and in scaling up NLP techniques to applications
> in information retrieval, relation extraction, and machine
> translation. He holds a Ph.D. in computer science from Johns Hopkins
> and an A.B. in classics from Harvard.
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