# Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation

arXiv: https://arxiv.org/abs/1602.04874 · HTML: https://mlsanity.com/paper/1602.04874/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2016.

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

- [Enhancing Identification of Structure Function of Academic Articles Using Contextual Information](https://mlsanity.com/paper/2111.14110/) (2021)
- [Local Translation Services for Neglected Languages](https://mlsanity.com/paper/2101.01628/) (2021)
- [Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models](https://mlsanity.com/paper/2505.19631/) (2025)
- [Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on Chinese Comment Text](https://mlsanity.com/paper/2108.00400/) (2021)

## Abstract (arXiv)

Recurrent neural network(RNN) has been broadly applied to natural language processing(NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory(LSTM) units for Chinese word segmentation, which is a crucial preprocess task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network(BLSTM) does not need any prior knowledge or pre-designing, and it is expert in keeping the contextual information in both directions. Experiment result shows that our approach gets state-of-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.