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LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis (2008.13173v1)

Published 30 Aug 2020 in cs.CL and cs.AI

Abstract: 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.

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Authors (5)
  1. Somnath Banerjee (22 papers)
  2. Sahar Ghannay (14 papers)
  3. Sophie Rosset (14 papers)
  4. Anne Vilnat (2 papers)
  5. Paolo Rosso (41 papers)
Citations (8)