BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction

Vachagan Gratian


Abstract
The paper describes an ensemble of linear perceptrons trained for emotion classification as part of the SemEval-2019 shared-task 3. The model uses a matrix of probabilities to weight the activations of the base-classifiers and makes a final prediction using the sum rule. The base-classifiers are multi-class perceptrons utilizing character and word n-grams, part-of-speech tags and sentiment polarity scores. The results of our experiments indicate that the ensemble outperforms the base-classifiers, but only marginally. In the best scenario our model attains an F-Micro score of 0.672, whereas the base-classifiers attained scores ranging from 0.636 to 0.666.
Anthology ID:
S19-2020
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:
137–141
Language:
URL:
https://aclanthology.org/S19-2020
DOI:
10.18653/v1/S19-2020
Bibkey:
Cite (ACL):
Vachagan Gratian. 2019. BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 137–141, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction (Gratian, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2020.pdf