Abstract
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human’s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.- Anthology ID:
- S18-1019
- 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:
- 141–145
- Language:
- URL:
- https://aclanthology.org/S18-1019
- DOI:
- 10.18653/v1/S18-1019
- Cite (ACL):
- Yanghoon Kim, Hwanhee Lee, and Kyomin Jung. 2018. AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 141–145, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification (Kim et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1019.pdf