# Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

arXiv: https://arxiv.org/abs/1905.01964 · HTML: https://mlsanity.com/paper/1905.01964/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

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

- [GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps](https://mlsanity.com/paper/2108.09104/) (2021)
- [Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting](https://mlsanity.com/paper/2204.11406/) (2022)
- [Application of Data Encryption in Chinese Named Entity Recognition](https://mlsanity.com/paper/2208.14627/) (2022)

## Abstract (arXiv)

Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly context-dependent. In addition, Chinese texts lack delimiters to separate words, making it difficult to identify the boundary of entities. Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming. In this paper, we propose a neural approach for CNER. First, we introduce a CNN-LSTM-CRF neural architecture to capture both local and long-distance contexts for CNER. Second, we propose a unified framework to jointly train CNER and word segmentation models in order to enhance the ability of CNER model in identifying entity boundaries. Third, we introduce an automatic method to generate pseudo labeled samples from existing labeled data which can enrich the training data. Experiments on two benchmark datasets show that our approach can effectively improve the performance of Chinese named entity recognition, especially when training data is insufficient.