The task focuses on predicting all sentiment graphs in a text, where a single sentiment graph is composed of a sentiment holder, target, expression and polarity.
Publication
Jeremy Barnes, Laura Oberlaender, Enrica Troiano, Andrey Kutuzov, Jan Buchmann, Rodrigo Agerri, Lilja Øvrelid, and Erik Velldal. 2022. SemEval 2022 Task 10: Structured Sentiment Analysis. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1280–1295, Seattle, United States. Association for Computational Linguistics.
Language
Spanish
English
NLP topic
Abstract task
Year
2022
Publication link
Ranking metric
F1
Task results
| System | Precision | Recall | F1 | CEM | Accuracy | MacroPrecision | MacroRecall | MacroF1 | RMSE | MicroPrecision | MicroRecall | MicroF1 | MAE | MAP | UAS | LAS | MLAS | BLEX | Pearson correlation | Spearman correlation | MeasureC | BERTScore | EMR | Exact Match | F0.5 | Hierarchical F | ICM | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 Sort ascending | WAC | b2 | erde30 | sent | weighted f1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MT-speech | 0.74 | ||||||||||||||||||||||||||||||||||||
| SLPL | 0.74 | ||||||||||||||||||||||||||||||||||||
| Hitachi | 0.73 | ||||||||||||||||||||||||||||||||||||
| zhixiaobao | 0.72 | ||||||||||||||||||||||||||||||||||||
| SeqL | 0.70 |
