Abstract
This paper presents the system in SemEval-2019 Task 3, “EmoContext: Contextual Emotion Detection in Text”. We propose a deep learning architecture with bidirectional LSTM networks, augmented with an emotion-oriented attention network that is capable of extracting emotion information from an utterance. Experimental results show that our model outperforms its variants and the baseline. Overall, this system has achieved 75.57% for the microaveraged F1 score.- Anthology ID:
- S19-2049
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 287–291
- Language:
- URL:
- https://aclanthology.org/S19-2049
- DOI:
- 10.18653/v1/S19-2049
- Cite (ACL):
- Luyao Ma, Long Zhang, Wei Ye, and Wenhui Hu. 2019. PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 287–291, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network (Ma et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/S19-2049.pdf