BDBC-KG-NLP / Ie Survey
北航大数据高精尖中心张日崇研究团队对信息抽取领域的调研。包括实体识别，关系抽取，属性抽取等子任务，每类子任务分别对学术界和工业界进行调研。
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IE-Survey
信息抽取（information extraction），即从自然语言文本中，抽取出特定的事件或事实信息，帮助我们将海量内容自动分类、提取和重构。这些信息通常包括实体（entity）、关系（relation）、事件（event）。例如从新闻中抽取时间、地点、关键人物，或者从技术文档中抽取产品名称、开发时间、性能指标等。 由于能从自然语言中抽取出信息框架和用户感兴趣的事实信息，无论是在知识图谱、信息检索、问答系统还是在情感分析、文本挖掘中，信息抽取都有广泛应用。
本次信息抽取领域学术界/工业界调研主要包括以下三个子任务：
- 实体抽取与链指：即命名实体识别
- 关系抽取：三元组(triple)抽取,即(Subject, Predication, Object)，主要用于抽取实体间的关系
- 属性抽取：相当于一种多元关系的抽取
希望能为自然语言处理信息抽取领域的相关学者和研究人员提供帮助。本survey将保持定期持续更新、持续跟踪前沿技术，如有不足请大家批评指正，欢迎各位信息抽取研究者取用，也欢迎大家共同完善此调研。
版本更新
Version 1.0：2020年10月30日
致谢
特此感谢支持数据公开与系统研发工作的北航高精尖中心及参与这项工作的各位团队成员：
参与贡献方式
欢迎pull requests。对于较大的更改，请先开issue以讨论您要更改的内容。
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