Spectral learning in non-deterministic dependency parsing
Document typeConference report
Rights accessRestricted access - publisher's policy
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 also present an inside-outside algorithm for the parsing model that runs in cubic time, hence maintaining the standard parsing costs for context-free grammars.
Best Paper Award of EACL 2012
CitationLuque, F. M. [et al.]. Spectral learning in non-deterministic dependency parsing. A: European Chapter of the Association for Computational Linguistics. "Proceedings of the 13th Conference of the European Chapter of the ACL (EACL 2012". Avignon: 2012, p. 409-419.