Abstract
This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text. The goal was to for a given textual dialogue, i.e. a user utterance along with two turns of context, identify the emotion of user utterance as one of the emotion classes: Happy, Sad, Angry or Others. Our system: ConSSED is a configurable combination of semantic and sentiment neural models. The official task submission achieved a micro-average F1 score of 75.31 which placed us 16th out of 165 participating systems.- Anthology ID:
- S19-2027
- 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:
- 175–179
- Language:
- URL:
- https://aclanthology.org/S19-2027
- DOI:
- 10.18653/v1/S19-2027
- Cite (ACL):
- Rafał Poświata. 2019. ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 175–179, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector (Poświata, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S19-2027.pdf
- Code
- rafalposwiata/conssed
- Data
- EmoContext