信息抽取技术在数字人文领域的应用研究综述

doi:10.11896/jsjkx.250600198

摘要/Abstract

摘要： 数字人文作为计算机科学与人文学科交叉融合的新兴领域,旨在通过数字技术解决人文研究中的问题,推动学科发展、文化遗产保护和文化传播。信息抽取技术作为自然语言处理的核心任务之一,能够从非结构化文本中自动提取结构化知识,为数字人文研究提供丰富的数据支持。该综述系统梳理了信息抽取技术在数字人文领域的应用研究,聚焦其三大子任务:命名实体识别、关系抽取和事件抽取。首先,研究梳理了各任务的发展历程和代表性工作,从早期的规则词典方法到传统机器学习,再到当前主流的深度学习和预训练语言模型方法,分析了技术演进的脉络。其次,探讨了数字人文领域信息抽取面临的主要挑战,如语料稀缺、文本结构复杂、实体边界模糊、关系表达隐晦等,并对现有方法的普适性和局限性进行了深入讨论。最后,展望了未来研究方向,包括多模态信息抽取、跨语言处理、低资源场景优化、知识图谱构建及语言生成技术等。该综述为信息抽取技术在数字人文领域的进一步研究和应用提供了理论支持和实践参考。

关键词: 数字人文, 自然语言处理, 信息抽取, 命名实体识别, 关系抽取, 事件抽取, 深度学习

Abstract: Digital humanities,as an emerging interdisciplinary field integrating computer science and humanities,aims to address research challenges in humanities through digital technologies,thereby advancing disciplinary development,cultural heritage preservation,and cultural dissemination.Information extraction,a core task in natural language processing,enables the automatic extraction of structured knowledge from unstructured texts,providing valuable data support for digital humanities research.This review systematically examines the applications of information extraction technologies in digital humanities,focusing on three key subtasks:named entity recognition,relation extraction,and event extraction.The study traces the evolution of these tasks from early rule-based and dictionary methods to traditional machine learning approaches,and further to current mainstream techniques based on deep learning and pre-trained language models,analyzing the trajectory of technological advancements.Furthermore,the review discusses the unique challenges of information extraction in digital humanities,including data scarcity,complex text structures,ambiguous entity boundaries,and implicit relationship expressions,while critically evaluating the applicability and limitations of existing methods.Finally,future research directions are outlined,such as multimodal information extraction,cross-lingual processing,optimization for low-resource scenarios,knowledge graph construction,and language generation technologies.The review offers theoretical insights and practical guidance for further research and applications of information extraction in digital humanities.

Key words: Digital humanities, Natural language processing, Information extraction, Named entity recognition, Relation extraction, Event extraction, Deep learning

中图分类号:

TP391

隗昊, 张宗煜, 刁宏悦, 邓耀臣. 信息抽取技术在数字人文领域的应用研究综述[J]. 计算机科学, 2025, 52(11A): 250600198-10. https://doi.org/10.11896/jsjkx.250600198

WEI Hao, ZHANG Zongyu, DIAO Hongyue, DENG Yaochen. Review of Application of Information Extraction Technology in Digital Humanities[J]. Computer Science, 2025, 52(11A): 250600198-10. https://doi.org/10.11896/jsjkx.250600198

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