知识与句法融合的因果关系抽取网络

汪诗蕊; 解博涵; 丁玲; 陈建廷; 向阳

doi:10.11959/j.issn.2096-0271.2024008

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知识与句法融合的因果关系抽取网络

研究 | 更新时间：2024-06-03

- 知识与句法融合的因果关系抽取网络
- Event causality identification network based on knowledge and syntactic structure
- 大数据 2024年10卷第3期页码：82-92
- 作者机构：
- 作者简介：
  
  [ "汪诗蕊（2001- ），女，同济大学电子与信息工程学院硕士生，主要研究方向为数据挖掘、知识图谱。" ]
  [ "解博涵（2000- ），女，同济大学电子与信息工程学院硕士生，主要研究方向为知识图谱、自然语言处理。" ]
  [ "丁玲（1995- ），女，同济大学电子与信息工程学院博士生，主要研究方向为自然语言处理、信息抽取。" ]
  [ "陈建廷（1995- ），男，博士，同济大学电子与信息工程学院博士后，主要研究方向为数据挖掘、大数据、深度学习。" ]
  [ "向阳（1962- ），男，博士，同济大学，教授，主要研究方向为数据挖掘、自然语言处理、知识图谱。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(72071145)
- DOI：10.11959/j.issn.2096-0271.2024008
  中图分类号： TP391.1
- 网络首发：2024-05，
  
  纸质出版：2024-05-15
- 稿件说明：
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汪诗蕊, 解博涵, 丁玲, 等. 知识与句法融合的因果关系抽取网络[J]. 大数据, 2024,10(3):82-92.

Shirui WANG, Bohan XIE, Ling DING, et al. Event causality identification network based on knowledge and syntactic structure[J]. Big data research, 2024, 10(3): 82-92.
汪诗蕊, 解博涵, 丁玲, 等. 知识与句法融合的因果关系抽取网络[J]. 大数据, 2024,10(3):82-92. DOI： 10.11959/j.issn.2096-0271.2024008.

Shirui WANG, Bohan XIE, Ling DING, et al. Event causality identification network based on knowledge and syntactic structure[J]. Big data research, 2024, 10(3): 82-92. DOI： 10.11959/j.issn.2096-0271.2024008.

摘要

因果关系抽取作为关系抽取的一个重要任务，近年来得到了广泛关注。现有的因果关系抽取方法大多将句法结构和背景知识割裂开进行研究，早期的因果关系抽取方法偏重于从句法结构层面进行分析，随着深度学习技术的发展，预训练模型结合背景知识的方法成为主流。然而上述两种方法均未完全融合句内信息和外部知识，带来了不同程度的信息缺失。为了解决这一问题，提出了结合句法结构和背景知识的因果关系抽取模型。该模型将句子解析为同时包含句法和知识的知识句法图结构，使用图卷积网络进行信息融合。模型同时考虑了句法和知识两部分信息，从而进一步丰富了实体嵌入，达到了良好的因果关系抽取效果。本模型在EventStoryLine数据集上取得了良好效果，F1值达到0.445，与现有方法相比提高了2.3%。

Abstract

Event causality identification is an important task of relationship extraction

which has received much attention recent years.Most of the existing methods separate syntactic structure from the background knowledge information.The early causality extraction methods focus on the analysis of syntactic structure level.With the development of deep learning

the methods that use the pre-training model combined with background knowledge has become the mainstream.However

neither of the above two kinds of methods fully integrates the sentence information and external knowledge

resulting in different degrees of information loss.To address this problem

we proposed a novel model of event causality identification combining syntactic structure and background knowledge.Our model parses sentences into knowledge syntactic graph structures that contain both syntax and knowledge

and uses the graph convolution network for information fusion.It considers both syntax and knowledge information

which further enriches the event representation and performs effectively.In experiments on the widely-used dataset EventStoryLine

the F1 score of our model achieves 0.445

a 2.3% improvement over existing methods.

关键词

Keywords

references

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