
Colloque du
Département d'informatique et de recherche opérationnelle
Hal Daumé III
University of Maryland
Predicting Linguistic Structures Accurately and Efficiently
Résumé :
Many classic problems in natural language processing can be cast as
building mapping from a complex input (e.g., a sequence of words) to a
complex output (e.g., a syntax tree or semantic graph). This task is
challenging both because language is ambiguous (learning difficulties)
and represented with discrete combinatorial structures (computational
difficulties). Often these are at odds: the features you want to add
to decrease learning difficulties cause nontrivial additional
structure yielding worse computational difficulties.
I will begin by discussing algorithms that side-step the issue of
combinatorial blowup and aim to predict an output structure directly.
I will then present approaches that explicitly learn to trade-off
accuracy and efficiency, applied to a variety of linguistic phenomena.
Moreover, I will show that in some cases, we can actually obtain a
model that is faster and more accurate by exploiting smarter learning
algorithms.
This is joint work with Jason Eisner, John Langford,
Jiarong Jiang, He He, Adam Teichert and Tim Vieira.
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Le jeudi 21 novembre 2013, 15h30, pavillon André-Aisenstadt, salle 3195
Information : Gilles Brassard, brassard@iro.umontreal.ca