EMNLP-2019/11-Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction

[BrambleXu]

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:

• pdf, 中文介绍
• code
• [paper-with-code](

Paper information:

• Author: Tsinghua University
• Dataset: Chinese financial announcements, data
• keywords:

Notes:

本文解决的问题示例：

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：

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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