Document-level relation extraction model based on anaphora and logical reasoning

doi:10.11772/j.issn.1001-9081.2024050676

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (5): 1496-1503.DOI: 10.11772/j.issn.1001-9081.2024050676

• Artificial intelligence • Previous Articles

Document-level relation extraction model based on anaphora and logical reasoning

Jie HU¹^,²^,³, Cui WU¹, Jun SUN¹^,²^,³(), Yan ZHANG¹^,²^,³

^1.School of Computer Science，Hubei University，Wuhan Hubei 430062，China
^2.Hubei Key Laboratory of Big Data Intelligent Analysis and Application （Hubei University），Wuhan Hubei 430062，China
^3.Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence （Hubei University），Wuhan Hubei 430062，China

Received:2024-05-27 Revised:2024-08-28 Accepted:2024-08-30 Online:2024-09-05 Published:2025-05-10
Contact: Jun SUN
About author:HU Jie， born in 1977， Ph. D.， professor. Her research interests include complex semantic big data management， natural language processing.
WU Cui， born in 2000， M. S. candidate. Her research interests include natural language processing.
SUN Jun， born in 1979， M. S.， lecturer. Her research interests include natural language processing.
ZHANG Yan， born in 1974，Ph. D.， professor. His research interests include software engineering， information security.
Supported by:
National Natural Science Foundation of China(61977021)

基于回指与逻辑推理的文档级关系抽取模型

胡婕¹^,²^,³, 吴翠¹, 孙军¹^,²^,³(), 张龑¹^,²^,³

^1.湖北大学计算机学院，武汉 430062
^2.大数据智能分析与行业应用湖北省重点实验室（湖北大学），武汉 430062
^3.智慧政务与人工智能应用湖北省工程研究中心（湖北大学），武汉 430062

通讯作者: 孙军
作者简介:胡婕（1977—），女，湖北汉川人，教授，博士，主要研究方向：复杂语义大数据管理、自然语言处理
吴翠（2000—），女，湖北荆州人，硕士研究生，主要研究方向：自然语言处理
孙军（1979—），女，湖北枣阳人，讲师，硕士，主要研究方向：自然语言处理
张龑（1974—），男，湖北宜昌人，教授，博士，CCF会员，主要研究方向：软件工程、信息安全。
基金资助:
国家自然科学基金资助项目(61977021)

Abstract

Abstract:

In Document-level Relation Extraction （DocRE） task， the existing models mainly focus on learning interaction among entities in the document， neglecting the learning of internal structures of entities， and pay little attention to recognition of pronoun references and application of logical rules in the document. The above leads to the model not being accurate enough in modeling relationships among entities in the document. Therefore， an anaphor-aware relation graph was integrated on the basis of the Transformer architecture to model interaction among entities and internal structures of entities. So that， anaphora was used to aggregate more contextual information to the corresponding entities， thereby enhancing relation extraction accuracy. Moreover， a data-driven approach was used to mine logical rules from relation annotations to enhance understanding and reasoning capabilities for implicit logical relationships in the text. To solve the problem of sample imbalance， a weighted long-tail loss function was introduced to improve the accuracy of identifying rare relations. Experiments were conducted on two public datasets DocRED （Document-level Relation Extraction Dataset） and Re?DocRED （Revisiting Document-level Relation Extraction Dataset）. The results show that the proposed model has the best performance， when using BERT as encoder， its IgnF1 and F1 values on test set of on DocRED are increased by 1.79 and 2.09 percentage points compared to those of the baseline model ATLOP （Adaptive Thresholding and Localized cOntext Pooling）， respectively， validating the high comprehensive performance of the proposed model.

Key words: Document-level Relation Extraction (DocRE), anaphor-aware relation graph, logical rule, sample imbalance, weighted long-tail loss function

摘要：

在文档级关系抽取（DocRE）任务中，现有模型主要侧重于学习文档中实体间的交互，忽略了对实体内部结构的学习，并很少关注到文档中的代词指代识别问题以及对逻辑规则的应用，这导致模型对文档中实体间关系的建模不够准确。因此，基于Transformer的架构融合关系回指图，建模实体间交互和实体内部结构，从而利用回指将更多上下文信息聚合到相应实体上以提高关系抽取的准确性。此外，采用数据驱动方式从关系注释中挖掘逻辑规则，增强对文本隐含逻辑关系的理解和推理能力。针对样本不平衡问题，引入加权长尾损失函数提高对稀有关系的识别准确性。在2个公开数据集DocRED（Document-level Relation Extraction Dataset）和Re-DocRED（Revisiting Document-level Relation Extraction Dataset）上的实验结果表明，所提模型性能表现最优，在DocRED测试集上，基于BERT编码器的模型的IgnF1和F1值比基线模型ATLOP（Adaptive Thresholding and Localized cOniext Pooling）分别提高了1.79和2.09个百分点，可见所提模型的综合性能较高。

关键词: 文档级关系抽取, 关系回指图, 逻辑规则, 样本不平衡, 加权长尾损失函数

CLC Number:

TP391

Jie HU, Cui WU, Jun SUN, Yan ZHANG. Document-level relation extraction model based on anaphora and logical reasoning[J]. Journal of Computer Applications, 2025, 45(5): 1496-1503.

胡婕, 吴翠, 孙军, 张龑. 基于回指与逻辑推理的文档级关系抽取模型[J]. 《计算机应用》唯一官方网站, 2025, 45(5): 1496-1503.

Figures/Tables 9

References 20

1	李冬梅，张扬，李东远，等.实体关系抽取方法研究综述［J］.计算机研究与发展，2020，57（7）：1424-1448.
	LI D M， ZHANG Y， LI D Y， et al. Review of entity relationship extraction methods［J］. Journal of Computer Research and Development， 2020， 57（7）： 1424-1448.
2	VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［C］// Proceedings of the 31th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2017： 6000-6010.
3	YAO Y， YE D， LI P， et al. DocRED： a large-scale document-level relation extraction dataset［C］// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg： ACL， 2019： 764-777.
4	TAN Q， XU L， BING L， et al. Revisiting DocRED — addressing the false negative problem in relation extraction［C］// Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg： ACL， 2022： 8472-8487.
5	冯钧，魏大保，苏栋，等. 文档级实体关系抽取方法研究综述［J］. 计算机科学， 2022， 49（10）：224-242.
	FENG J， WEI D B， SU D， et al. Survey of document-level entity relationship extraction methods［J］. Computer Science， 2022， 49（10）： 224-242.
6	祝涛杰，卢记仓，周刚，等. 文档级关系抽取技术研究综述［J］. 计算机科学， 2023， 50（5）：189-200.
	ZHU T J， LU J C， ZHOU G， et al. Review of document-level relation extraction techniques［J］. Computer Science， 2023， 50（5）：189-200.
7	DEVLIN J， CHANG M W， LEE K， et al. BERT： pre-training of deep bidirectional Transformers for language understanding［C］// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics： Human Language Technologies， Volume 1 （Long and Short Papers）. Stroudsburg： ACL， 2019： 4171-4186.
8	LIU Y， OTT M， GOYAL N， et al. RoBERTa： a robustly optimized BERT pretraining approach［EB/OL］. ［2023-11-18］..
9	ZHOU W， HUANG K， MA T， et al. Document-level relation extraction with adaptive thresholding and localized context pooling［C］// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2021： 14612-14620.
10	HUANG K， QI P， WANG G， et al. Entity and evidence guided document-level relation extraction［C］// Proceedings of the 6th Workshop on Representation Learning for NLP. Stroudsburg： ACL， 2021： 307-315.
11	XIE Y， SHEN J， LI S， et al. Eider： empowering document-level relation extraction with efficient evidence extraction and inference-stage fusion［C］// Findings of the Association for Computational Linguistics： ACL 2022. Stroudsburg： ACL， 2022： 257-268.
12	MA Y， WANG A， OKAZAKI N. DREEAM： guiding attention with evidence for improving document-level relation extraction［C］// Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg： ACL， 2023： 1971-1983.
13	LU C， ZHANG R， SUN K， et al. Anaphor assisted document-level relation extraction［C］// Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg： ACL， 2023： 15453-15464.
14	ZHANG N， CHEN X， XIE X， et al. Document-level relation extraction as semantic segmentation［C］// Proceedings of the 30th International Joint Conference on Artificial Intelligence. San Francisco： Morgan Kaufmann Publishers Inc.， 2021： 3999-4006.
15	XIAO Y， ZHANG Z， MAO Y， et al. SAIS： supervising and augmenting intermediate steps for document-level relation extraction［C］// Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics： Human Language Technologies. Stroudsburg： ACL， 2022： 2395-2409.
16	QI J， XU B， ZENG K， et al. ConstGCN： constrained transmission-based graph convolutional networks for document-level relation extraction［EB/OL］. ［2023-08-14］..
17	ZENG S， XU R， CHANG B， et al. Double graph based reasoning for document-level relation extraction［C］// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg： ACL， 2020： 1630-1640.
18	KIPF T N， WELLING M. Semi-supervised classification with graph convolutional networks［EB/OL］. ［2023-07-18］..
19	XUE Z， LI R， DAI Q， et al. CorefDRE： document-level relation extraction with coreference resolution［EB/OL］. ［2023-05-25］..
20	ZHANG Y， ZHONG V， CHEN D， et al. Position-aware attention and supervised data improve slot filling［C］// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg： ACL， 2017： 35-45.

数据集	集合	文档数	平均实体数	平均元组数	平均句子数
DocRED	train	3 053	19.5	12.5	7.9
DocRED	dev	1 000	19.6	12.3	8.1
Re-DocRED	train	3 053	19.4	28.1	7.9
	dev	500	19.4	34.6	8.2
	test	500	19.6	34.9	7.9

数据集	集合	文档数	平均实体数	平均元组数	平均句子数
DocRED	train	3 053	19.5	12.5	7.9
DocRED	dev	1 000	19.6	12.3	8.1
Re-DocRED	train	3 053	19.4	28.1	7.9
	dev	500	19.4	34.6	8.2
	test	500	19.6	34.9	7.9

编码器	模型	开发集			测试集
编码器	模型	IgnF1	F1	EviF1	IgnF1	F1	EviF1
BERT	ATLOP^［9］	59.22	61.09	—	59.31	61.30	—
	E2GRE^［10］	55.22	58.72	47.12	—	—	—
	DocuNet^［14］	59.86	61.83	—	59.93	61.86	—
	CorefDRE^［19］	60.78	60.82		54.54	56.96
	SAIS^［15］	59.98	62.96	53.70	60.96	62.77	52.88
	Eider^［11］	60.51	62.48	50.71	60.42	62.47	51.27
	DREEAM^［12］	60.51	62.55	52.08	60.03	62.49	51.71
	AA^［13］	61.31	63.38	—	60.84	63.10	—
	本文模型	61.43	63.54	52.53	61.10	63.39	52.66
RoBERTa	ATLOP^［9］	61.32	63.18	—	61.39	63.40	—
	E2GRE^［10］	—	—	—	60.30	62.50	50.5
	DocuNet^［14］	62.23	64.12	—	62.39	64.55	—
	SAIS^［15］	62.23	65.17	55.84	63.44	65.11	55.67
	Eider^［11］	62.34	64.27	52.54	62.85	64.79	53.01
	DREEAM^［12］	62.29	64.20	54.15	62.12	64.27	54.01
	AA^［13］	63.15	65.19	—	62.88	64.98	—
	本文模型	63.37	65.35	54.77	63.01	65.30	54.58

编码器	模型	开发集			测试集
编码器	模型	IgnF1	F1	EviF1	IgnF1	F1	EviF1
BERT	ATLOP^［9］	59.22	61.09	—	59.31	61.30	—
	E2GRE^［10］	55.22	58.72	47.12	—	—	—
	DocuNet^［14］	59.86	61.83	—	59.93	61.86	—
	CorefDRE^［19］	60.78	60.82		54.54	56.96
	SAIS^［15］	59.98	62.96	53.70	60.96	62.77	52.88
	Eider^［11］	60.51	62.48	50.71	60.42	62.47	51.27
	DREEAM^［12］	60.51	62.55	52.08	60.03	62.49	51.71
	AA^［13］	61.31	63.38	—	60.84	63.10	—
	本文模型	61.43	63.54	52.53	61.10	63.39	52.66
RoBERTa	ATLOP^［9］	61.32	63.18	—	61.39	63.40	—
	E2GRE^［10］	—	—	—	60.30	62.50	50.5
	DocuNet^［14］	62.23	64.12	—	62.39	64.55	—
	SAIS^［15］	62.23	65.17	55.84	63.44	65.11	55.67
	Eider^［11］	62.34	64.27	52.54	62.85	64.79	53.01
	DREEAM^［12］	62.29	64.20	54.15	62.12	64.27	54.01
	AA^［13］	63.15	65.19	—	62.88	64.98	—
	本文模型	63.37	65.35	54.77	63.01	65.30	54.58

模型	IgnF1	F1	模型	IgnF1	F1
ATLOP	76.94	77.73	AA	80.12	81.20
DocuNet	77.27	77.92	本文模型	82.13	83.26
DREEAM	79.66	80.73

Document-level relation extraction model based on anaphora and logical reasoning

基于回指与逻辑推理的文档级关系抽取模型

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 20

Related Articles 15

Recommended Articles

Metrics

模型	F1	IgnF1	EviF1
本文模型	63.54	61.43	52.53
-关系回指图中的信息聚合层	63.29	61.23	52.45
-关系回指图	62.89	60.88	51.52
-逻辑规则	63.02	60.94	52.01
-加权长尾损失函数	63.25	61.16	52.48

置信度	DocRED		Re-DocRED
置信度	F1/%	IgnF1/%	F1/%	IgnF1/%
0.55	64.88	62.95	82.79	81.71
0.60	64.92	63.01	82.97	81.87
0.65	65.13	63.21	83.26	82.13
0.70	65.35	63.37	83.15	82.02
0.75	65.21	63.21	83.10	81.86
0.80	65.11	63.18	83.01	81.79

[1]	WU Jun, ZHAO Chuan. Small object detection method based on improved DETR algorithm [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	. Ground penetrating radar clutter suppression algorithm for airport runways [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	SHEN Bin, CHEN Xiaoning, CHENG Hua, FANG Yiquan, WANG Huifeng. Intelligent undergraduate teaching evaluation system based on large language models [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	Haiyan TIAN, Saihao HUANG, Dong ZHANG, Shoushan LI. Visually guided word segmentation and part of speech tagging [J]. Journal of Computer Applications, 2025, 45(5): 1488-1495.
[5]	Hui LI, Bingzhi JIA, Chenxi WANG, Ziyu DONG, Jilong LI, Zhaoman ZHONG, Yanyan CHEN. Generative adversarial network underwater image enhancement model based on Swin Transformer [J]. Journal of Computer Applications, 2025, 45(5): 1439-1446.
[6]	Wenbin HU, Tianxiang CAI, Tianle HAN, Zhaoman ZHONG, Changxia MA. Multimodal sarcasm detection model integrating contrastive learning with sentiment analysis [J]. Journal of Computer Applications, 2025, 45(5): 1432-1438.
[7]	Sijie NIU, Yuliang LIU. Auxiliary diagnostic method for retinopathy based on dual-branch structure with knowledge distillation [J]. Journal of Computer Applications, 2025, 45(5): 1410-1414.
[8]	Jun YANG, Mengxue PANG, Lishan QIAO. Estimation and classification of brain functional networks based on temporal correlation information fusion [J]. Journal of Computer Applications, 2025, 45(5): 1403-1409.
[9]	Bo XU, Dezhi HAO, Erchen YU, Hongfei LIN, Linlin ZONG. Psychological counseling human-machine dialogue dataset construction for dialogue generation and mental disorder detection [J]. Journal of Computer Applications, 2025, 45(5): 1395-1402.
[10]	Pengyu CHEN, Xiushan NIE, Nanjun LI, Tuo LI. Semi-supervised video object segmentation method based on spatio-temporal decoupling and regional robustness enhancement [J]. Journal of Computer Applications, 2025, 45(5): 1379-1386.
[11]	Yufei LONG, Yuchen MOU, Ye LIU. Multi-source data representation learning model based on tensorized graph convolutional network and contrastive learning [J]. Journal of Computer Applications, 2025, 45(5): 1372-1378.
[12]	Wei ZONG, Yue ZHAO, Yin LI, Xiaona XU. Review of optimization methods for end-to-end speech-to-speech translation [J]. Journal of Computer Applications, 2025, 45(5): 1363-1371.
[13]	LIU Xiaoxia, KUANG Liqun, WANG Song, JIAO Shichao, HAN Huiyan, XIONG Fengguang. Multi-scale spatio-temporal decoupling for contrastive learning of skeleton action recognition [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	YIN Chunyong, ZHANG Bufan. Multi-scale based multivariate time series anomaly detection model [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	CHEN Xiaolei, ZHENG Zhiwei, HUANG Xue, QU Zhenbin. Panoramic video super-resolution network with joint spherical alignment and adaptive geometric correction [J]. Journal of Computer Applications, 0, (): 0-0.

模型	Inter-F1	Intra-F1
本文模型	56.34	68.44
-关系回指图	55.51	68.21

模型	Inter-F1	Intra-F1
本文模型	56.34	68.44
-关系回指图	55.51	68.21

损失函数	频繁类	长尾类	整体
加权长尾损失函数	64.26	39.75	63.54
ATL	64.23	39.01	63.26

损失函数	频繁类	长尾类	整体
加权长尾损失函数	64.26	39.75	63.54
ATL	64.23	39.01	63.26