# Component-Enhanced Chinese Character Embeddings

arXiv: https://arxiv.org/abs/1508.06669 · HTML: https://mlsanity.com/paper/1508.06669/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2015.

## Built on by (most cited first, 8 of 8)

- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [CC-Riddle: A Question Answering Dataset of Chinese Character Riddles](https://mlsanity.com/paper/2206.13778/) (2022)
- [Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling](https://mlsanity.com/paper/2211.12781/) (2022)
- [Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification](https://mlsanity.com/paper/2212.08105/) (2022)
- [Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training](https://mlsanity.com/paper/2305.18760/) (2023)
- [Character-level Chinese Backpack Language Models](https://mlsanity.com/paper/2310.12751/) (2023)
- [Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slip Scripts](https://mlsanity.com/paper/2409.01011/) (2024)
- [An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex](https://mlsanity.com/paper/2203.15173/) (2022)

## Abstract (arXiv)

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions. Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our models.