Tübingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction

Çağrı Çöltekin, Taraka Rama


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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1004.pdf