NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep

Parag Agrawal, Anshuman Suri


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/S19-2045.pdf
Code
 iamgroot42/nelec
Data
EmoContext