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dc.contributor.authorTaslimipoor, Shiva
dc.contributor.authorRohanian, Omid
dc.contributor.authorMože, Sara
dc.date.accessioned2019-07-02T08:16:42Z
dc.date.available2019-07-02T08:16:42Z
dc.date.issued2019-06-06
dc.identifier.citationTaslimipoor, S., Rohanian, O. and Može, S. (2019) GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks, Proceedings of the 13th International Workshop on Semantic Evaluation. Minneapolis, Minnesota, USA: Association for Computational Linguistics, pp. 102–106.en
dc.identifier.urihttp://hdl.handle.net/2436/622503
dc.description.abstractThis paper describes the system submitted to the SemEval 2019 shared task 1 ‘Cross-lingual Semantic Parsing with UCCA’. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure of the output along with the types of relations among the nodes. Our proposed neural architecture is composed of Graph Convolution and BiLSTM components. The layers of the system share their weights while predicting dependency links and semantic labels. The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing.en
dc.formatapplication/PDFen
dc.language.isoenen
dc.publisherAssociation for Computational Linguisticsen
dc.relation.urlhttps://aclweb.org/anthology/papers/S/S19/S19-2014/en
dc.subjectNatural Language Processingen
dc.subjectSemantic Parsingen
dc.subjectDeep Learningen
dc.subjectGraph Convolutional Neural Networksen
dc.subjectSemantic Dependency Parsingen
dc.titleGCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networksen
dc.typeConference contributionen
dc.date.updated2019-07-01T16:01:11Z
dc.conference.nameSemEval 2019
dc.date.accepted2019-04-05
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW020719STen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2019-07-02en
refterms.dateFCD2019-07-02T08:16:28Z
refterms.versionFCDAM
refterms.dateFOA2019-07-02T08:16:42Z


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