Papers › Multi-Task Identification of Entities, Relations, and Coreference for Scientific...
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Yi Luan, Luheng He, Mari Ostendorf, Hannaneh Hajishirzi
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Joint Entity and Relation Extraction | SciERC | SciIE | Cross Sentence | No | #8 of 11 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | SciERC | SciIE | Entity F1 | 64.20 | #8 of 11 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | SciERC | SciIE | Relation F1 | 39.30 | #8 of 11 | Archive leaderboard | report |
| Named Entity Recognition (NER) | SciERC | SCIIE | F1 | 64.20 | #7 of 7 | Archive leaderboard | report |
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