Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

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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Semantic Scholar SemEval-2017 Task-10
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Joint Entity and Relation Extraction SciERC SciIE Entity F1 64.20 # 11
Relation F1 39.30 # 8
Cross Sentence No # 1
Named Entity Recognition (NER) SciERC SCIIE F1 64.20 # 8


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