共指
计算机科学
任务(项目管理)
鉴定(生物学)
图形
判决
知识图
信息抽取
人工智能
自然语言处理
领域(数学分析)
情报检索
分辨率(逻辑)
理论计算机科学
生物
植物
数学分析
经济
管理
数学
作者
Yi Luan,Luheng He,Mari Ostendorf,Hannaneh Hajishirzi
摘要
We introduce a multi-task setup of identifying 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 SciIE 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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