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Automatic analysis for modern Chinese has greatly improved the accuracy oftext mining in related fields, but the study of ancient Chinese is stillrelatively rare. Ancient text division and lexical annotation are importantparts of classical literature comprehension, and previous studies have tried toconstruct auxiliary dictionary and other fused knowledge to improve theperformance. In this paper, we propose a framework for ancient Chinese WordSegmentation and Part-of-Speech Tagging that makes a twofold effort: on the onehand, we try to capture the wordhood semantics; on the other hand, were-predict the uncertain samples of baseline model by introducing externalknowledge. The performance of our architecture outperforms pre-trained BERTwith CRF and existing tools such as Jiayan.

The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS