Event causality identification network based on knowledge and syntactic structure

Shirui WANG; Bohan XIE; Ling DING; Jianting CHEN; Yang XIANG

doi:10.11959/j.issn.2096-0271.2024008

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Event causality identification network based on knowledge and syntactic structure

STUDY | 更新时间：2024-06-03

- Event causality identification network based on knowledge and syntactic structure
- Big data research Vol. 10, Issue 3, Pages: 82-92(2024)
- 作者机构：
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- DOI：10.11959/j.issn.2096-0271.2024008
  CLC： TP391.1
- Online First：2024-05，
  
  Published：15 May 2024
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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：

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

GIRJU R . Automatic detection of causal relations for Question Answering [C ] // Proceedings of the ACL 2003 workshop on Multilingual Summarization and Question Answering . Morristown:Association for Computational Linguistics , 2003 : 76 - 83 .

OH J H , TORISAWA K , HASHIMOTO C , et al . A semi-supervised learning approach to why-question answering [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2016 , 30 ( 1 ).

HASHIMOTO C , TORISAWA K , KLOETZER J , et al . Toward future scenario generation:extracting event causality exploiting semantic relation,context,and association features [C ] // Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2014 : 987 - 997 .

BERANT J , SRIKUMAR V , CHEN P C , et al . Modeling biological processes for reading comprehension [C ] // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2014 : 1499 - 1510 .

KHOO C S G , KORNFILT J , ODDY R N , et al . Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing [J ] . Literary and Linguistic Computing , 1998 , 13 ( 4 ): 177 - 186 .

GIRJU R , MOLDOVAN D . Text mining for causal relations [C ] // Proceedings of the 15th International Florida Artificial Intelligence Research Society Conference . Palo Alto:AAAI Press , 2002 : 360 - 364 .

GIRJU R . Automatic detection of causal relations for Question Answering [C ] // Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering . Morristown:Association for Computational Linguistics , 2003 : 76 - 83 .

LUO Z , SHA Y , ZHU K Q , et al . Commonsense causal reasoning between short texts [C ] // Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning . Palo Alto:AAAI Press , 2016 : 421 - 430 .

DE SILVA T N , XIAO Z , ZHAO R , et al . Causal relation identification using convolutional neural networks and knowledge based features [J ] . World Academy of Science,Engineering and Technology:International Journal of Mechanical and Mechatronics Engineering , 2017 , 11 ( 6 ): 703 - 708 .

KRUENGKRAI C , TORISAWA K , HASHIMOTO C , et al . Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2017 , 31 ( 1 ): 3466 - 3473 .

LI P , MAO K . Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts [J ] . Expert Systems with Applications , 2019 , 115 : 512 - 523 .

DASGUPTA T , SAHA R , DEY L , et al . Automatic extraction of causal relations from text using linguistically informed deep neural networks [C ] // Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue . Stroudsburg,PA,USA:Association for Computational Linguistics , 2018 : 306 - 316 .

LIU J , CHEN Y B , ZHAO J . Knowledge enhanced event causality identification with mention masking generalizations [C ] // Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence . New York:ACM , 2021 : 3608 - 3614 .

MIWA M , BANSAL M . End-to-end relation extraction using LSTMs on sequences and tree structures [C ] // Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2016 : 1105 - 1116 .

ZHANG Y H , QI P , MANNING C D . Graph convolution over pruned dependency trees improves relation extraction [C ] // Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2018 : 2205 - 2215 .

SUN K , ZHANG R C , MAO Y Y , et al . Relation extraction with convolutional network over learnable syntax-transport graph [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 5 ): 8928 - 8935 .

CHEN G M , TIAN Y H , SONG Y , et al . Relation extraction with type-aware map memories of word dependencies [C ] // Proceedings of Findings of the Association for Computational Linguistics:ACLIJCNLP 2021 . Stroudsburg:Association for Computational Linguistics , 2021 : 2501 - 2512 .

CASELLI T , VOSSEN P . The event StoryLine corpus:a new benchmark for causal and temporal relation extraction [C ] // Proceedings of the Events and Stories in the News Workshop . Stroudsburg:Association for Computational Linguistics , 2017 .

CHENG F , MIYAO Y . Classifying temporal relations by bidirectional LSTM over dependency paths [C ] // Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2017 : 1 - 6 .

CHOUBEY P K , HUANG R H . A sequential model for classifying temporal relations between intra-sentence events [C ] // Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2017 : 1796 - 1802 .

GAO L , CHOUBEY P K , HUANG R H . Modeling document-level causal structures for event causal relation identification [C ] // Proceedings of the 2019 Conference of the North . Stroudsburg:Association for Computational Linguistics , 2019 : 1808 - 1817 .

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