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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.167.pdf
- Code
- IamAdiSri/hcms-semeval20