Abstract
Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually, especially for low-resource languages, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.- Anthology ID:
- 2020.coling-main.517
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5879–5890
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.517
- DOI:
- 10.18653/v1/2020.coling-main.517
- Cite (ACL):
- Arun Ramachandran and Gerard de Melo. 2020. Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5879–5890, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (Ramachandran & de Melo, COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.517.pdf