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Syntactically enriched multilingual sentiment analysis
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-07-16T11:57:38Z | |
dc.date.available | 2024-07-16T11:57:38Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | C. Gómez-Rodríguez, "Syntactically enriched multilingual sentiment analysis", CEUR Workshop Proceedings, Vol. 2693, pp. 5 - 6, 2020, 2020 Workshop on Hybrid Intelligence for Natural Language Processing Tasks, HI4NLP 2020, Santiago de Compostela, 29 August 2020. | es_ES |
dc.identifier.issn | 1613-0073 | |
dc.identifier.uri | http://hdl.handle.net/2183/38061 | |
dc.description | Proceedings of the 2020 Workshop on Hybrid Intelligence for Natural Language Processing Tasks, HI4NLP (co-located at ECAI-2020), Santiago de Compostela, August 29, 2020, published at http://ceur-ws.org | es_ES |
dc.description | Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). | es_ES |
dc.description.abstract | [Abstract]: Sentiment analysis of natural language texts needs to deal with linguistic phenomena like negation, intensification or adversative clauses. In this talk, I present an approach to tackle such phenomena by means of syntactic information. Our approach combines machine learning and symbolic processing: the former is used to obtain dependency trees for input sentences, and the latter to obtain the sentiment polarity for each sentence using handwritten rules that traverse the tree. Thanks to universal guidelines for syntactic annotation, our approach is applicable to multiple languages without rewriting the rules. Additionally, very accurate parsing is not needed for our approach to be helpful: fast and simple parsers will do, even if they lag behind state-of-the-art accuracy. | es_ES |
dc.description.sponsorship | This work has received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia and ERDF (ED431B 2017/01, ED431G 2019/01). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2017/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | CEUR-WS | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/714150 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85160-C2-1-R/ES/AVANCES EN NUEVOS SISTEMAS DE EXTRACCION DE RESPUESTAS CON ANALISIS SEMANTICO Y APRENDIZAJE PROFUNDO | es_ES |
dc.relation.uri | https://ceur-ws.org/Vol-2693/invited2.pdf | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Dependency trees | es_ES |
dc.subject | Linguistic phenomena | es_ES |
dc.subject | Multiple languages | es_ES |
dc.subject | Natural language text | es_ES |
dc.subject | State of the art | es_ES |
dc.subject | Symbolic processing | es_ES |
dc.subject | Syntactic annotation | es_ES |
dc.subject | Syntactic information | es_ES |
dc.title | Syntactically enriched multilingual sentiment analysis | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | CEUR Workshop Proceedings | es_ES |
UDC.volume | 2693 | es_ES |
UDC.startPage | 5 | es_ES |
UDC.endPage | 6 | es_ES |
UDC.conferenceTitle | HI4NLP 2020 | es_ES |
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