融合对抗学习的因果关系抽取

冯冲; 康丽琪; 石戈; 黄河燕

doi:10.16383/j.aas.2018.c170481

融合对抗学习的因果关系抽取

doi: 10.16383/j.aas.2018.c170481

冯冲^1,2, ,,
康丽琪^1,2,,
石戈^1,2,,
黄河燕^1,2,

1.
北京理工大学计算机学院北京 100081
2.
北京理工大学自然语言处理实验室北京 100081

详细信息

作者简介:
康丽琪  北京理工大学计算机学院硕士研究生.主要研究方向为信息抽取, 关系抽取.E-mail:klq207@126.com

石戈  北京理工大学计算机学院博士研究生.主要研究方向为信息抽取, 事件抽取.E-mail:shige@bit.edu.cn

黄河燕  北京理工大学计算机学院教授.1989年获中国科学院计算技术研究所计算机科学与技术博士学位.主要研究方向为自然语言处理和机器翻译社交网络与信息检索, 智能处理系统.E-mail:hhy63@bit.edu.cn

通讯作者:
冯冲北京理工大学计算机学院副研究员.2005年获中国科学技术大学计算机科学系博士学位.主要研究方向为自然语言处理, 信息抽取, 机器翻译.本文通信作者.E-mail:fengchong@bit.edu.cn

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出版历程
- 收稿日期: 2017-08-31
- 录用日期: 2018-01-08
- 刊出日期: 2018-05-20

Causality Extraction With GAN

FENG Chong^{1,2
, ,},
KANG Li-Qi^{1,2
,},
SHI Ge^{1,2
,},
HUANG He-Yan^{1,2
,}

1.
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081
2.
Natural LanguageProcessing Laboratory, Beijing Institute of Technology, Beijing 100081

More Information

Author Bio:
Master student at the College of Computer Science and Technology, Beijing Institute of Technology. Her research interest covers information extraction, relation extraction

Ph. D. candidate at the College of Computer Science and Technology, Beijing Institute of Technology. His research interest covers information extraction, event extraction

Professor at the College of Computer Science and Technology, Beijing Institute of Technology. She received her Ph. D. degree from the Institute of Computing Technology, Chinese Academy of Sciences. Her research interest covers natural language processing, machine translation, social network, information retrieval, and intelligent processing system

Corresponding author: FENG Chong Associate professor at the College of Computer Science and Technology, Beijing Institute of Technology. He received his Ph. D. degree from the Department of Computer Science, University of Science and Technology of China in 2005. His research interest covers natural language processing, information extraction, and machine translation. Corresponding author of this paper

摘要

摘要: 因果关系抽取在事件预测、情景生成、问答以及文本蕴涵等任务上都有重要的应用价值.但多数现有的因果关系抽取方法都需要人工定义模式和约束，且严重依赖知识库.为此，本文利用生成式对抗网络（Generative adversarial networks，GAN）的对抗学习特性，将带注意力机制的双向门控循环单元神经网络（Bidirectional gated recurrent units networks，BGRU）与对抗学习相融合，通过重定义生成模型和判别模型，基本的因果关系抽取网络能够与判别网络形成对抗，进而从因果关系解释信息中获得高区分度的特征.实验结果表明，与当前用于因果关系抽取的方法相比较，该方法表现出更优的抽取效果.
- 因果关系抽取 /
- 生成式对抗网络 /
- 注意力机制 /
- 对抗学习
Abstract: Causality extraction is of important practical value in tasks such as event prediction, scenario generation, question answering, and textual implication; but most of the existing causality extraction methods require artificial definition of patterns and constraints and are heavily dependent on knowledge base. In this paper, the bidirectional gated recurrent units networks (BGRU) with attention mechanism are merged with confrontational learning by leveraging the confrontational learning characteristics of generative adversarial networks (GAN). Through redefining the generator and discriminator, the basic causality extraction network can construct a confrontation with the discriminator, and then obtain a high distinguishing feature from the causality interpretation information. Our experiments show that our approach leads to an improved performance over strong baselines.
- Causality extraction /
- generative adversarial network (GAN) /
- attention mechanism /
- adversarial learning
注释:

1) 本文责任编委李力

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图 1 GAN结构

Fig. 1 The structure of GAN

下载: 全尺寸图片幻灯片

图 2 模型整体架构

Fig. 2 The overall architecture of the model

下载: 全尺寸图片幻灯片

图 3 带注意力机制的双向门控循环单元神经网络

Fig. 3 Bidirectional GRU model with attention

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图 4 不同模型的对比实验

Fig. 4 Comparative experiment of different models

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表 1 数据集的构造说明

Table 1 Description of the dataset

关系类型	数据来源	数据条数
因果关系	SemEval	1 331
因果关系	新闻语料人工标注	700
非因果关系	SemEval	1 900

下载: 导出CSV

表 2 BGRU因果关系抽取结果(%)

Table 2 Results of BGRU causality extraction (%)

Model	P	R	F1
B-BGRU	92.93	85.98	89.32
R-BGRU	93.74	94.39	94.06

下载: 导出CSV

表 3 GAN框架下的因果关系抽取(%)

Table 3 Causality extraction under GAN framework (%)

Model	P	R	F1
B-BGRU	92.93	85.98	89.32
GAN-BGRU	93.75	87.62	90.58

下载: 导出CSV

表 4 带注意力机制的GAN框架下的因果关系抽取(%)

Table 4 Causality extraction under GAN framework with attention (%)

Model	P	R	F1
B-Att-BGRU	92.91	88.79	90.80
R-Att-BGRU	94.63	94.63	94.63
GAN-Att-BGRU	93.17	89.25	91.17

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