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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/S19-2050.pdf