2026-04-17T21:14:36Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/276912025-12-19T23:00:55Zcom_2072_6col_2072_452952

urn:hdl:10230/27691 Visualizing deep-syntactic parser output Soler Company, Juan Ballesteros, Miguel Bohnet, Bernd Mille, Simon Wanner, Leo Tractament del llenguatge natural (Informàtica) Lingüística computacional Comunicació presentada a la 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015), celebrada del 31 de maig al 5 de juny 2015 a Denver (CO, EUA). “Deep-syntactic” dependency structures bridge the gap between the surface-syntactic structures as produced by state-of-the-art dependency parsers and semantic logical forms in that they abstract away from surfacesyntactic idiosyncrasies, but still keep the linguistic structure of a sentence. They have thus a great potential for such downstream applications as machine translation and summarization. In this demo paper, we propose an online version of a deep-syntactic parser that outputs deep-syntactic structures from plain sentences and visualizes them using the Brat tool. Along with the deep syntactic structures, the user can also inspect the visual presentation of the surface-syntactic structures that serve as input to the deep-syntactic parser and that are produced by the joint tagger and syntactic transition-based parser ran in the pipeline before deep-syntactic parsing takes place. This work has been partially funded by the European Union’s Seventh Framework and Horizon 2020 Research and Innovation Programmes under the Grant Agreement numbers FP7-ICT-610411, FP7-SME- 606163, and H2020-RIA-645012. 2016-12-05T08:36:11Z 2016-12-05T08:36:11Z 2015 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Mihalcea R, Chai J, Anoop S, editors. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2015 May 31 - June 5; Denver, Colorado, United States. [Stroudsburg]: ACL; 2015. p. 56-60. info:eu-repo/grantAgreement/EC/FP7/610411 info:eu-repo/grantAgreement/EC/FP7/606163 info:eu-repo/grantAgreement/EC/H2020/645012 © ACL, Creative Commons Attribution 4.0 License http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess ACL (Association for Computational Linguistics)