LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
| Autor: | Sahar Ghannay, Paolo Rosso, Anne Vilnat, Somnath Banerjee, Sophie Rosset |
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| Přispěvatelé: | Information, Langue Ecrite et Signée (ILES), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Universitat Politècnica de València (UPV), Daly, Bénédicte |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
FOS: Computer and information sciences Computer Science - Artificial Intelligence Computer science 02 engineering and technology computer.software_genre Semantics Convolutional neural network Task (project management) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.EIAH] Computer Science [cs]/Technology for Human Learning 0202 electrical engineering electronic engineering information engineering Computer Science - Computation and Language business.industry Sentiment analysis SemEval Recurrent neural network Artificial Intelligence (cs.AI) 020201 artificial intelligence & image processing [INFO.EIAH]Computer Science [cs]/Technology for Human Learning Artificial intelligence business F1 score computer Computation and Language (cs.CL) Natural language processing Test data |
| Zdroj: | HAL SemEval@COLING |
| Popis: | This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix Hindi-English subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent Convolutional Neural Network that combines both the recurrent neural network and the convolutional network to better capture the semantics of the text, for code-mixed sentiment analysis. The proposed system obtained 0.69 (best run) in terms of F1 score on the given test data and achieved the 9th place (Codalab username: somban) in the SentiMix Hindi-English subtask. Comment: To be published in the Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020), Barcelona, Spain, Sep. Association for Computational Linguistics |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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