AMORE-UPF at SemEval-2018 Task 4: BiLSTM with entity library

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dc.contributor.author	Boleda, Gemma
dc.contributor.author	Aina, Laura
dc.contributor.author	Silberer, Carina
dc.contributor.author	Sorodoc, Ionut-Teodor
dc.contributor.author	Westera, Matthijs
dc.date	2018
dc.identifier.citation	Aina L, Silberer C, Sorodoc IT, Westera M, Boleda G. AMORE-UPF at SemEval-2018 Task 4: BiLSTM with entity library. In: Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018); 2018 Jun 5-6; New Orleans, LA. Stroudsburg (PA): ACL; 2018. p. 65–9.
dc.identifier.uri	http://hdl.handle.net/10230/35468
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	ACL (Association for Computational Linguistics)
dc.relation	Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018); 2018 Jun 5-6; New Orleans, LA. Stroudsburg (PA): ACL; 2018. p. 65–9.
dc.relation	info:eu-repo/grantAgreement/EC/H2020/715154
dc.rights	© ACL, Creative Commons Attribution 4.0 License
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by/4.0/
dc.subject	Computational linguistics
dc.subject	Natural language processing
dc.subject	Computational semantics
dc.subject	Semantic evaluation
dc.subject	Distributional semantics
dc.subject	Reference
dc.subject	Entities
dc.title	AMORE-UPF at SemEval-2018 Task 4: BiLSTM with entity library
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/publishedVersion
dc.description.abstract	Comunicació presentada al 12th International Workshop on Semantic Evaluation (SemEval-2018), celebrat els dies 5 i 6 de juny de 2018 a Nova Orleans, EUA.
dc.description.abstract	This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. It is a simple, standard model with one key innovation, an entity library. Our results show that this innovation greatly facilitates the identification of infrequent characters. Because of the generic nature of our model, this finding is potentially relevant to any task that requires effective learning from sparse or unbalanced data.
dc.description.abstract	This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 715154), and from the Spanish Ramón y Cajal programme (grant RYC-2015-18907).

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