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.
Cite
CITATION STYLE
Ma, L., Zhang, L., Ye, W., & Hu, W. (2019). PKUSE at SemEval-2019 task 3: Emotion detection with emotion-oriented neural attention network. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 287–291). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2049
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.