@inproceedings{srivastava-vardhan-2020-hcms,
title = "{HCMS} at {S}em{E}val-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts",
author = "Srivastava, Aditya and
Vardhan, V. Harsha",
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-1.167",
doi = "10.18653/v1/2020.semeval-1.167",
pages = "1253--1258",
abstract = "Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1{\%}, we demonstrate that simple convolution and attention may well produce reasonable results.",
}
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<abstract>Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.</abstract>
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%0 Conference Proceedings
%T HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts
%A Srivastava, Aditya
%A Vardhan, V. Harsha
%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 srivastava-vardhan-2020-hcms
%X Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.
%R 10.18653/v1/2020.semeval-1.167
%U https://aclanthology.org/2020.semeval-1.167
%U https://doi.org/10.18653/v1/2020.semeval-1.167
%P 1253-1258
Markdown (Informal)
[HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts](https://aclanthology.org/2020.semeval-1.167) (Srivastava & Vardhan, SemEval 2020)
ACL