PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Spectral Learning for Non-Deterministic Dependency Parsing
Franco M. Luque, Ariadna Quattoni, Borja Balle and Xavier Carreras
In: 13th Conference of the European Chapter of the Association for Computational Linguistics, 23-27 April 2012, Avignon, France.


In this paper we study spectral learning methods for non-deterministic split head-automata grammars, a powerful hidden-state formalism for dependency parsing. We present a learning algorithm that, like other spectral methods, is efficient and non-susceptible to local minima. We show how this algorithm can be formulated as a technique for inducing hidden structure from distributions computed by forward-backward recursions. Furthermore, we present an inside-outside algorithm for the parsing model that runs in cubic time, hence maintaining the standard parsing costs for context-free grammars.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:8970
Deposited By:Xavier Carreras
Deposited On:21 February 2012