张力元;王军;

• 1:北京大学图书馆
• 2:北京大学信息管理系

摘要(Abstract)：

目录是组织与利用古籍资源的重要工具，也是图书情报学科的重点研究对象。互著与别裁作为古典目录学中的两种辅助方法，能在深入剖析文献内容特征的基础上，根据内容的多元性将文献准确、完整地记载于目录体系中，达到“类例既分，学术自明”的效果。将互著与别裁映射为文本挖掘中的文本分类问题，提出基于机器学习以实现互著与别裁的方法框架，为古籍在目录体系中的多类目记载提供方法。首先利用TextCNN与BERT两种机器学习模型对先秦诸子六家十部典籍文本进行分类训练，结果显示BERT优于TextCNN,可以达到91.64%的分类准确率；之后用微调训练后的BERT模型对《荀子》与《管子》进行篇、章粒度的分类判断，最终得出这两部图书各篇章互著与别裁的结果。本研究展现了在数字人文视域下，数字技术对古典目录学、古典文献学以及学术史研究的应用价值。图5。表7。参考文献43。

关键词(KeyWords)： 古籍目录;互著;别裁;机器学习;数字人文

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金国际重点合作项目“中国儒家学术史知识图谱构建研究”(编号：72010107003)的研究成果之一~~

作者(Authors): 张力元;王军;

DOI: 10.13530/j.cnki.jlis.2022014

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