Abstract
This paper describes our participation in the SemEval-2018 task Multilingual Emoji Prediction. We participated in both English and Spanish subtasks, experimenting with support vector machines (SVMs) and recurrent neural networks. Our SVM classifier obtained the top rank in both subtasks with macro-averaged F1-measures of 35.99% for English and 22.36% for Spanish data sets. Similar to a few earlier attempts, the results with neural networks were not on par with linear SVMs.- Anthology ID:
- S18-1004
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–38
- Language:
- URL:
- https://aclanthology.org/S18-1004
- DOI:
- 10.18653/v1/S18-1004
- Cite (ACL):
- Çağrı Çöltekin and Taraka Rama. 2018. Tübingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 34–38, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Tübingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction (Çöltekin & Rama, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1004.pdf