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Título:	Data-driven deep-syntactic dependency parsing
Autor/a:	Ballesteros, Miguel; Bohnet, Bernd; Mille, Simon; Wanner, Leo
Abstract:	‘Deep-syntactic’ dependency structures that capture the argumentative, attributive and co-/nordinative relations between full words of a sentence have a great potential for a number/nof NLP-applications. The abstraction degree of these structures is in between the output/nof a syntactic dependency parser (connected trees defined over all words of a sentence and/nlanguage-specific grammatical functions) and the output of a semantic parser (forests of trees/ndefined over individual lexemes or phrasal chunks and abstract semantic role labels which/ncapture the frame structures of predicative elements and drop all attributive and coordinative/ndependencies). We propose a parser that provides deep-syntactic structures. The parser has/nbeen tested on Spanish, English and Chinese
Abstract:	The work reported on in this paper has been partially funded by the European Commission under the contract numbers FP7-ICT-610411 (MULTISENSOR) and H2020-645012-RIA (KRISTINA).
Materia(s):	-Processament del llenguatge natural -Tractament del llenguatge natural (Informàtica) -Lingüística computacional
Derechos:	info:eu-repo/semantics/embargoedAccess © Cambridge University Press. The published version of the article: Ballesteros M, Bohnet B, Mille S, Wanner L. Data-driven deep-syntactic dependency parsing. Nat Lang Eng. 2016 Nov;22(6):939-74. http://dx.doi.org/10.1017/S1351324915000285
Tipo de documento:	Artículo Artículo - Versión aceptada
Editor:	Cambridge University Press
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