基于上下文感知的智能合约要素提取方法

钱肖; 蒋忠元; 陶梅悦; 刘柄呈; 李任翔; 高胜; 马建峰

doi:10.12263/DZXB.20241038

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基于上下文感知的智能合约要素提取方法

学术论文 | 更新时间：2025-07-24

- 基于上下文感知的智能合约要素提取方法
- A Context-Aware Approach for Smart Contract Element Extraction
- 电子学报 2025年53卷第4期页码：1322-1336
- 作者机构：
  
  1.西安电子科技大学网络与信息安全学院，陕西西安，710126
  2.中央财经大学信息学院，北京，100081
- 作者简介：
  
  [ "钱肖男，1999年出生于浙江省湖州市.现为西安电子科技大学网络与信息安全学院硕士研究生.主要研究方向为自然语言处理.E-mail: xqian@stu.xidian.edu.cn" ]
  [ "蒋忠元男，1988年出生于陕西省榆林市.现为西安电子科技大学网络与信息安全学院教授、博士生导师.主要研究方向为复杂（无人）网络系统安全、大数据与AI安全、空天地一体化网络技术、法律自然语言处理.E-mail: zyjiang@xidian.edu.cn" ]
  [ "陶梅悦女，2001年出生于安徽省芜湖市.现为西安电子科技大学网络与信息安全学院硕士研究生.主要研究方向为软件可靠性、异常检测.E-mail: mytao@xidian.stu.edu.cn" ]
  [ "刘柄呈男，2001年出生于甘肃省庆阳市.现为西安电子科技大学网络与信息安全学院硕士研究生.主要研究方向为自然语言处理.E-mail: bingchengliu@stu.xidian.edu.cn" ]
  [ "李任翔男，2001年出生于河南省漯河市.现为西安电子科技大学网络与信息安全学院硕士研究生.主要研究方向为自然语言处理和智能合约.E-mail: renxiangli@stu.xidian.edu.cn" ]
  [ "高胜男，1987年出生于湖北省黄冈市.现为中央财经大学信息学院教授、博士生导师.主要研究方向为区块链、智能合约、联邦计算和大语言模型安全.E-mail: sgao@cufe.edu.cn" ]
  [ "马建峰男，1963年出生于陕西省西安市.现为西安电子科技大学网络与信息安全学院教授、博士生导师.主要研究方向为网络安全、系统安全、数据安全和无人机安全.中国电子学会会员编号：E190004733F.E-mail: jfma@mail.xidian.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2022YFB2701800)
- DOI：10.12263/DZXB.20241038
  中图分类号： TP391;
- 收稿：2024-11-17，
  
  修回：2025-03-17，
  
  纸质出版：2025-04-25
- 稿件说明：
移动端阅览
钱肖, 蒋忠元, 陶梅悦, 等. 基于上下文感知的智能合约要素提取方法[J]. 电子学报, 2025, 53(04): 1322-1336.

QIAN Xiao, JIANG Zhong-yuan, TAO Mei-yue, et al. A Context-Aware Approach for Smart Contract Element Extraction[J]. Acta Electronica Sinica, 2025, 53(04): 1322-1336.
钱肖, 蒋忠元, 陶梅悦, 等. 基于上下文感知的智能合约要素提取方法[J]. 电子学报, 2025, 53(04): 1322-1336. DOI：10.12263/DZXB.20241038

QIAN Xiao, JIANG Zhong-yuan, TAO Mei-yue, et al. A Context-Aware Approach for Smart Contract Element Extraction[J]. Acta Electronica Sinica, 2025, 53(04): 1322-1336. DOI：10.12263/DZXB.20241038

摘要

针对各行各业海量文本文档的智能合约化需求，提取文本关键数据要素是首要基础.与传统命名实体识别（Named Entity Recognition，NER）相比，合约要素提取（Contract Element Extraction，CEE）技术旨在提取泛在较长、更多样、较冗余合约要素，然而目前面临着中文研究不足、对新颖大语言模型（Large Language Model，LLM）技术应用不够充分、对文本上下文关联特征感知不足等挑战.本文首先提出了新颖的上下文语义感知动态填充方法（Context-sensitive Dynamic Padding Method，CDPM）、三重注意力层和要素边缘加权损失函数模块，在不增加硬件需求的前提下，为模型提供额外上下文语义信息，增强对上下文关联特征的感知能力，从而提升基于序列标注范式的CEE训练效率.其次，融合上述模块和BERT（Bidirectional Enc

oder Representations from Transformers）嵌入模型构建了一种基于上下文感知的合约要素提取模型（Context-Aware Model for Contract Element Extraction，CAM-CEE），实现了面向智能合约化场景的高性能要素提取.最后，在本文自主构建的数据集以及相关公开数据集上进行了大量实验.结果表明，本文提出框架CAM-CEE在micro

、macro

等指标上的性能超越最佳基线模型，并具有高通用性.

Abstract

Extracting key data elements from text is the primary foundation for the intelligent contract conversion demand of massive text documents in various industries. Compared with traditional named entity recognition (NER)

contract element extraction (CEE) aims to extract ubiquitous

lengthy

diverse

and redundant contract elements. However

it faces challenges such as limited research in Chinese

lack of application of novel large language model (LLM) techniques

and insufficient perception of contextual features in text. This article first proposes a novel context-sensitive dynamic padding method (CDPM)

a triple attention layer

and an edge-weighted loss function. They provide additional context semantics without increasing hardware requirements

enhance the perception of context related features

and improve the efficiency of element extraction training under the sequential annotation paradigm; Secondly

a context-aware deep learning framework context-aware model for contract element extraction (CAM-CEE) was proposed by integrating the above modules with the bidirectional encoder representations from transformers (BERT) embedding model

achieving high-performance element extraction for smart contract scenarios; Finally

extensive experiments are conducted on the independently constructed and publicly available datasets in this article. The results indicate that the proposed framework CAM-CEE outperforms the best baseline model in metrics such as micro

and macro

and has high generality.

关键词

Keywords

references

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