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arXiv 2008.13173cs.CLcs.AI

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

LIMSI, CNRS, Université Paris-Saclay(LIMSI实验室,法国国家科学研究中心,巴黎-萨克雷大学);Universitat Politècnica de València(瓦伦西亚理工大学)

Somnath Banerjee, Sahar Ghannay, Sophie Rosset, Anne Vilnat, Paolo Rosso

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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