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EMNLP-2019/11-Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction #304
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BrambleXu opened 4 years ago
Summary:
端对端的中文金融领域事件抽取(event extraction, EE)框架。普通的EE方法只能在句子级别上抽取事件。但是这种模型对于跨句子的文档效果不好。本文提出的Doc2EDAG,是基于entity-based directed acyclic graph的,能实现文档级别的抽取。
Resource:
Paper information:
Notes:
本文解决的问题示例:![image](https://user-images.githubusercontent.com/10768193/77278842-b3608c80-6d03-11ea-837c-f3b7ec6763a6.png)
Dateset
数据是从ChFinAnn收集的2008-2018,关注5个事件类型:Equity Freeze (EF), Equity Repurchase (ER), Equity Underweight (EU), Equity Overweight (EO) and Equity Pledge (EP),
Model Graph:
entity-based directed acyclic graph (EDAG)的示例:
整个模型的workflow:![image](https://user-images.githubusercontent.com/10768193/77278818-a5127080-6d03-11ea-8307-95d3cf282ca3.png)
Result::
Thoughts:
5种事件类型里,最高的也才87%,其他基本70%,实用性不大
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