Abstract
Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc. makes existing deep-learning solutions perform poorly. The inability of deep-learning systems to robustly capture these covariates puts a cap on their performance. We propose NELEC: Neural and Lexical Combiner, a system which elegantly combines textual and deep-learning based methods for sentiment classification. We evaluate our system as part of the third task of ‘Contextual Emotion Detection in Text’ as part of SemEval-2019. Our system performs significantly better than the baseline, as well as our deep-learning model benchmarks. It achieved a micro-averaged F1 score of 0.7765, ranking 3rd on the test-set leader-board. Our code is available at https://github.com/iamgroot42/nelec- Anthology ID:
- S19-2045
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 266–271
- Language:
- URL:
- https://aclanthology.org/S19-2045
- DOI:
- 10.18653/v1/S19-2045
- Cite (ACL):
- Parag Agrawal and Anshuman Suri. 2019. NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 266–271, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep (Agrawal & Suri, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/S19-2045.pdf
- Code
- iamgroot42/nelec
- Data
- EmoContext