  Automatic analysis for modern Chinese has greatly improved the accuracy of
text mining in related fields, but the study of ancient Chinese is still
relatively rare. Ancient text division and lexical annotation are important
parts of classical literature comprehension, and previous studies have tried to
construct auxiliary dictionary and other fused knowledge to improve the
performance. In this paper, we propose a framework for ancient Chinese Word
Segmentation and Part-of-Speech Tagging that makes a twofold effort: on the one
hand, we try to capture the wordhood semantics; on the other hand, we
re-predict the uncertain samples of baseline model by introducing external
knowledge. The performance of our architecture outperforms pre-trained BERT
with CRF and existing tools such as Jiayan.
