Abstract
This paper presents our contextual emotion detection system in approaching the SemEval2019 shared task 3: EmoContext: Contextual Emotion Detection in Text. This system cooperates with an emotion detection neural network method (Poria et al., 2017), emoji2vec (Eisner et al., 2016) embedding, word2vec embedding (Mikolov et al., 2013), and our proposed emoticon and emoji preprocessing method. The experimental results demonstrate the usefulness of our emoticon and emoji prepossessing method, and representations of emoticons and emoji contribute model’s emotion detection.- Anthology ID:
- S19-2061
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 350–354
- Language:
- URL:
- https://aclanthology.org/S19-2061
- DOI:
- 10.18653/v1/S19-2061
- Cite (ACL):
- Zhishen Yang, Sam Vijlbrief, and Naoaki Okazaki. 2019. TokyoTech_NLP at SemEval-2019 Task 3: Emotion-related Symbols in Emotion Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 350–354, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- TokyoTech_NLP at SemEval-2019 Task 3: Emotion-related Symbols in Emotion Detection (Yang et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-2061.pdf
- Data
- EmoContext