Abstract
This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media — more precisely, Twitter — with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users’ tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.- Anthology ID:
- S18-1071
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 449–453
- Language:
- URL:
- https://aclanthology.org/S18-1071
- DOI:
- 10.18653/v1/S18-1071
- Cite (ACL):
- Liyuan Zhou, Qiongkai Xu, Hanna Suominen, and Tom Gedeon. 2018. EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 449–453, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption (Zhou et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/S18-1071.pdf