首页|基于自动生成句法模板的方法类实体关系抽取——以CSDN人工智能主题博客为例

基于自动生成句法模板的方法类实体关系抽取——以CSDN人工智能主题博客为例

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[目的/意义]方法类实体与应用场景、问题、组织机构等实体之间存在着较多的关联关系,抽取这些实体关系有助于捕捉技术发展趋势,助力创新能力提升。[方法/过程]文章探讨了一种基于自动生成句法模板的方法类实体关系抽取方法,通过设计一种新的自适应模板,提高抽取的灵活性和适应性,降低对大规模标注数据的依赖。文章实证以CSDN人工智能主题博客为例,使用少量种子三元组迭代生成句法模板抽取方法类实体及其关系,并通过过滤器模型提高抽取质量。[结果/结论]经过 5 轮迭代抽取,模型抽取三元组的精确度达到 55。2%,优于现有通用模型。研究结果表明,该方法能够高效利用有限的标注数据,实现特定领域方法类实体及其关系的高效抽取,为学术界和产业界的科技情报分析提供支持。
Method Entity and Relation Extraction Based on Automatically Generat-ed Syntactic Templates:A Case Study of CSDN Artificial Intelligence Blog
[Purpose/significance]There are many relationships between method entities and application scenarios,problems,organizations and other entities.Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates.By designing a new adaptive template,the method improves flexibility and adaptability,reducing dependence on large-scale labeled data.Using a small number of seed triples,the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog.It also improves the extraction quality using a filter model.[Result/conclusion]After 5 rounds of iterative extraction,the triplet extraction accuracy of the model reaches 55.2%,which is better than the existing general model.The results show that this method can effectively use the limited labeled data to extract method entities and their relationships in specific fields,and provide support for scientific and technological information analysis in academia and industry.

entity relation Extractionmethod entityautomatic generationsyntactic templateseed learning

李奎良、化柏林

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北京大学信息管理系,北京 100871

实体关系抽取 方法类实体 自动生成 句法模板 种子学习

2025

科技情报研究

科技情报研究

ISSN:
年,卷(期):2025.7(1)