Spectral learning in non-deterministic dependency parsing

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/20170

dc.contributor	Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor	Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor	Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.contributor.author	Luque, Franco M.
dc.contributor.author	Quattoni, Ariadna Julieta
dc.contributor.author	Balle Pigem, Borja de
dc.contributor.author	Carreras Pérez, Xavier
dc.date	2012
dc.identifier.citation	Luque, 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.
dc.identifier.uri	http://hdl.handle.net/2117/20170
dc.language.iso	eng
dc.relation	http://cataleg.upc.edu/record=b1267381~S1*cat
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
dc.subject	Spectral learning methods
dc.title	Spectral learning in non-deterministic dependency parsing
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	Best Paper Award of EACL 2012
dc.description.abstract	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.
dc.description.abstract	Peer Reviewed
dc.description.abstract	Award-winning

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