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

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

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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)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S19-2050.pdf
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