Abstract
This paper describes the approach developed by the ELiRF-UPV team at SemEval 2019 Task 3: Contextual Emotion Detection in Text. We have developed a Snapshot Ensemble of 1D Hierarchical Convolutional Neural Networks to extract features from 3-turn conversations in order to perform contextual emotion detection in text. This Snapshot Ensemble is obtained by averaging the models selected by a Genetic Algorithm that optimizes the evaluation measure. The proposed ensemble obtains better results than a single model and it obtains competitive and promising results on Contextual Emotion Detection in Text.- Anthology ID:
- S19-2031
- 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:
- 195–199
- Language:
- URL:
- https://aclanthology.org/S19-2031
- DOI:
- 10.18653/v1/S19-2031
- Cite (ACL):
- José-Ángel González, Lluís-F. Hurtado, and Ferran Pla. 2019. ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 195–199, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection (González et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/S19-2031.pdf