Person names and location names are essential building blocks for identifying events and social networks in historical documents that were written in literary Chinese. We take the lead to explore the research on algorithmically recognizing named entities in literary Chinese for historical studies with language-model based and conditional-random-field based methods, and extend our work to mining the document structures in historical documents. Practical evaluations were conducted with texts that were extracted from more than 220 volumes of local gazetteers (Difangzhi). Difangzhi is a huge and the single most important collection that contains information about officers who served in local government in Chinese history. Our methods performed very well on these realistic tests. Thousands of names and addresses were identified from the texts. A good portion of the extracted names match the biographical information currently recorded in the China Biographical Database (CBDB) of Harvard University, and many others can be verified by historians and will become as new additions to CBDB.

本文首次开展了基于语言模型和条件随机场的算法识别文言文历史文本中命名实体的研究，并将其扩展至历史文献的文档结构挖掘。通过对220余本地志文献的实际评估，该方法表现出很好的效果，成功识别出数千个名称和地址，其中许多与中国传记数据库中记录的个人信息相匹配，成为该数据库中的新近记录。

基于CRF与模式匹配的文言方志挖掘方法研究——以中国历史人物传记信息为例