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

TL;DR

This paper introduces an uncertainty-based framework for ancient Chinese word segmentation and POS tagging, leveraging external knowledge to improve accuracy beyond existing models like BERT with CRF.

Contribution

It proposes a novel framework that captures wordhood semantics and re-predicts uncertain samples using external knowledge, enhancing ancient Chinese text analysis.

Findings

Outperforms BERT with CRF in accuracy

Improves ancient Chinese text segmentation and tagging

Effectively utilizes external knowledge for uncertain samples

Abstract

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.