# Radical-Enhanced Chinese Character Embedding

arXiv: https://arxiv.org/abs/1404.4714 · HTML: https://mlsanity.com/paper/1404.4714/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2014.

## Built on by (most cited first, 9 of 9)

- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [Sub-Character Tokenization for Chinese Pretrained Language Models](https://mlsanity.com/paper/2106.00400/) (2021)
- [For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network](https://mlsanity.com/paper/2302.00419/) (2023)
- [Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training](https://mlsanity.com/paper/2305.18760/) (2023)
- [Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slip Scripts](https://mlsanity.com/paper/2409.01011/) (2024)
- [StyleBERT: Chinese pretraining by font style information](https://mlsanity.com/paper/2202.09955/) (2022)
- [An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex](https://mlsanity.com/paper/2203.15173/) (2022)
- [Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next](https://mlsanity.com/paper/2212.04068/) (2022)
- [CNM-BERT: A Drop-In Structural Embedding for Chinese Characters via Ideographic Description Sequences](https://mlsanity.com/paper/2608.05167/) (2026)

## Abstract (arXiv)

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.