A second-order joint Eisner model for syntactic and semantic dependency parsing

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/9228

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.author	Lluis Martorell, Xavier
dc.contributor.author	Bott, Stefan Markus
dc.contributor.author	Màrquez Villodre, Lluís
dc.date	2009
dc.identifier.citation	Lluis, X.; Bott, S.; Màrquez, L. A second-order joint Eisner model for syntactic and semantic dependency parsing. A: Conference on Natural Language Learning. "Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009)". 2009, p. 79-84.
dc.identifier.citation	978-1-932432-29-9
dc.identifier.uri	http://hdl.handle.net/2117/9228
dc.language.iso	eng
dc.relation	http://www.aclweb.org/anthology-new/W/W09/W09-1212.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subject	Computational linguistics -- Research
dc.subject	Natural language processing (Computer science)
dc.subject	Llenguatge natural (Informàtica) -- Processament
dc.subject	Lingüística computacional
dc.title	A second-order joint Eisner model for syntactic and semantic dependency parsing
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	We present a system developed for the CoNLL-2009 Shared Task (Hajic et al., 2009). We extend the Carreras (2007) parser to jointly annotate syntactic and semantic dependencies. This state-of-the-art parser factorizes the built tree in second-order factors. We include semantic dependencies in the factors and extend their score function to combine syntactic and semantic scores. The parser is coupled with an on-line averaged perceptron (Collins, 2002) as the learning method. Our averaged results for all seven languages are 71.49 macro F1, 79.11 LAS and 63.06 semantic F1.
dc.description.abstract	Peer Reviewed

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