HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts

Aditya Srivastava, V. Harsha Vardhan


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.
Anthology ID:
2020.semeval-1.167
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1253–1258
Language:
URL:
https://aclanthology.org/2020.semeval-1.167
DOI:
10.18653/v1/2020.semeval-1.167
Bibkey:
Cite (ACL):
Aditya Srivastava and V. Harsha Vardhan. 2020. HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1253–1258, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts (Srivastava & Vardhan, SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.167.pdf
Code
 IamAdiSri/hcms-semeval20