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

arXiv: https://arxiv.org/abs/2310.08496 · HTML: https://mlsanity.com/paper/2310.08496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [Adversarial Multi-Criteria Learning for Chinese Word Segmentation](https://mlsanity.com/paper/1704.07556/) (2017)
- [Subword Encoding in Lattice LSTM for Chinese Word Segmentation](https://mlsanity.com/paper/1810.12594/) (2018)
- [Unified Multi-Criteria Chinese Word Segmentation with BERT](https://mlsanity.com/paper/2004.05808/) (2020)
- [TURNER: The Uncertainty-based Retrieval Framework for Chinese NER](https://mlsanity.com/paper/2202.09022/) (2022)
- [A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder](https://mlsanity.com/paper/1906.12035/) (2019)

## Built on by (most cited first, 3 of 3)

- [SeqXGPT: Sentence-Level AI-Generated Text Detection](https://mlsanity.com/paper/2310.08903/) (2023)
- [InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling](https://mlsanity.com/paper/2508.15791/) (2025)
- [From Inline Notes to Collected Commentaries: Toward Context-Preserving Organization of Exegetical Knowledge in Classical Chinese Texts](https://mlsanity.com/paper/2607.29044/) (2026)

## Abstract (arXiv)

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.