# Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Distant Supervision

arXiv: https://arxiv.org/abs/2303.01912 · HTML: https://mlsanity.com/paper/2303.01912/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Understanding and Improving Early Stopping for Learning with Noisy Labels](https://mlsanity.com/paper/2106.15853/) (2021)
- [Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection](https://mlsanity.com/paper/1607.01133/) (2016)

## Built on by (most cited first, 2 of 2)

- [GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts](https://mlsanity.com/paper/2307.05354/) (2023)
- [InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling](https://mlsanity.com/paper/2508.15791/) (2025)

## Abstract (arXiv)

Ancient Chinese word segmentation (WSG) and part-of-speech tagging (POS) are important to study ancient Chinese, but the amount of ancient Chinese WSG and POS tagging data is still rare. In this paper, we propose a novel augmentation method of ancient Chinese WSG and POS tagging data using distant supervision over parallel corpus. However, there are still mislabeled and unlabeled ancient Chinese words inevitably in distant supervision. To address this problem, we take advantage of the memorization effects of deep neural networks and a small amount of annotated data to get a model with much knowledge and a little noise, and then we use this model to relabel the ancient Chinese sentences in parallel corpus. Experiments show that the model trained over the relabeled data outperforms the model trained over the data generated from distant supervision and the annotated data. Our code is available at https://github.com/farlit/ACDS.