# Neural Word Segmentation Learning for Chinese

arXiv: https://arxiv.org/abs/1606.04300 · HTML: https://mlsanity.com/paper/1606.04300/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System](https://mlsanity.com/paper/2304.01559/) (2023)
- [Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification](https://mlsanity.com/paper/2212.08105/) (2022)
- [Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models](https://mlsanity.com/paper/2505.19631/) (2025)
- [Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language](https://mlsanity.com/paper/2110.13032/) (2021)
- [Tangut Word Segmentation under Extreme Resource Scarcity: Integrating Traditional Lexicons and Unlabeled Text](https://mlsanity.com/paper/2608.18437/) (2026)

## Abstract (arXiv)

Most previous approaches to Chinese word segmentation formalize this problem as a character-based sequence labeling task where only contextual information within fixed sized local windows and simple interactions between adjacent tags can be captured. In this paper, we propose a novel neural framework which thoroughly eliminates context windows and can utilize complete segmentation history. Our model employs a gated combination neural network over characters to produce distributed representations of word candidates, which are then given to a long short-term memory (LSTM) language scoring model. Experiments on the benchmark datasets show that without the help of feature engineering as most existing approaches, our models achieve competitive or better performances with previous state-of-the-art methods.