Podlab at SemEval-2019 Task 3: The Importance of Being Shallow

Andrew Nguyen, Tobin South, Nigel Bean, Jonathan Tuke, Lewis Mitchell


Abstract
This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92% of the top score for the competition, demonstrating that “shallow” classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis.
Anthology ID:
S19-2050
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:
292–296
Language:
URL:
https://aclanthology.org/S19-2050
DOI:
10.18653/v1/S19-2050
Bibkey:
Cite (ACL):
Andrew Nguyen, Tobin South, Nigel Bean, Jonathan Tuke, and Lewis Mitchell. 2019. Podlab at SemEval-2019 Task 3: The Importance of Being Shallow. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 292–296, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow (Nguyen et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2050.pdf