Abstract
We introduce the UTFPR system for the Implicit Emotions Shared Task of 2018: A compositional character-to-word recurrent neural network that does not exploit heavy and/or hard-to-obtain resources. We find that our approach can outperform multiple baselines, and offers an elegant and effective solution to the problem of orthographic variance in tweets.- Anthology ID:
- W18-6224
- Volume:
- Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 176–181
- Language:
- URL:
- https://aclanthology.org/W18-6224
- DOI:
- 10.18653/v1/W18-6224
- Cite (ACL):
- Gustavo Paetzold. 2018. UTFPR at IEST 2018: Exploring Character-to-Word Composition for Emotion Analysis. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 176–181, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- UTFPR at IEST 2018: Exploring Character-to-Word Composition for Emotion Analysis (Paetzold, WASSA 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W18-6224.pdf