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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/S19-2020.pdf