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3 months ago
ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector
{Rafa{\l} Po{\'s}wiata}
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.
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| emotion-recognition-in-conversation-on-ec | ConSSED | Micro-F1: 0.7664 |
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