# Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding

arXiv: https://arxiv.org/abs/1810.03479 · HTML: https://mlsanity.com/paper/1810.03479/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search](https://mlsanity.com/paper/2602.22983/) (2026)
- [Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE](https://mlsanity.com/paper/2310.09550/) (2023)
- [GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts](https://mlsanity.com/paper/2307.05354/) (2023)
- [Punctuation restoration Model and Spacing Model for Korean Ancient Document](https://mlsanity.com/paper/2312.11881/) (2023)

## Abstract (arXiv)

In this paper, we develop a low than character feature embedding called radical embedding, and apply it on LSTM model for sentence segmentation of pre modern Chinese texts. The datasets includes over 150 classical Chinese books from 3 different dynasties and contains different literary styles. LSTM CRF model is a state of art method for the sequence labeling problem. Our new model adds a component of radical embedding, which leads to improved performances. Experimental results based on the aforementioned Chinese books demonstrates a better accuracy than earlier methods on sentence segmentation, especial in Tang Epitaph texts.