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

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.

Tasks

Reproductions