@inproceedings{baruah-etal-2020-iiitg-adbu,
title = "{IIITG}-{ADBU} at {S}em{E}val-2020 Task 9: {SVM} for Sentiment Analysis of {E}nglish-{H}indi Code-Mixed Text",
author = "Baruah, Arup and
Das, Kaushik and
Barbhuiya, Ferdous and
Dey, Kuntal",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.121",
doi = "10.18653/v1/2020.semeval-1.121",
pages = "946--950",
abstract = "In this paper, we present the results that the team IIITG-ADBU (codalab username {`}abaruah{'}) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342.",
}
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<abstract>In this paper, we present the results that the team IIITG-ADBU (codalab username ‘abaruah’) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342.</abstract>
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%0 Conference Proceedings
%T IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text
%A Baruah, Arup
%A Das, Kaushik
%A Barbhuiya, Ferdous
%A Dey, Kuntal
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F baruah-etal-2020-iiitg-adbu
%X In this paper, we present the results that the team IIITG-ADBU (codalab username ‘abaruah’) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342.
%R 10.18653/v1/2020.semeval-1.121
%U https://aclanthology.org/2020.semeval-1.121
%U https://doi.org/10.18653/v1/2020.semeval-1.121
%P 946-950
Markdown (Informal)
[IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text](https://aclanthology.org/2020.semeval-1.121) (Baruah et al., SemEval 2020)
ACL