# VCWE: Visual Character-Enhanced Word Embeddings

arXiv: https://arxiv.org/abs/1902.08795 · HTML: https://mlsanity.com/paper/1902.08795/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2019.

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

- [Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge](https://mlsanity.com/paper/2104.02704/) (2021)
- [SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media](https://mlsanity.com/paper/2211.08447/) (2022)
- [An In-depth Study on Internal Structure of Chinese Words](https://mlsanity.com/paper/2106.00334/) (2021)
- [Quantifying Phonosemantic Iconicity Distributionally in 6 Languages](https://mlsanity.com/paper/2510.14040/) (2025)

## Abstract (arXiv)

Chinese is a logographic writing system, and the shape of Chinese characters contain rich syntactic and semantic information. In this paper, we propose a model to learn Chinese word embeddings via three-level composition: (1) a convolutional neural network to extract the intra-character compositionality from the visual shape of a character; (2) a recurrent neural network with self-attention to compose character representation into word embeddings; (3) the Skip-Gram framework to capture non-compositionality directly from the contextual information. Evaluations demonstrate the superior performance of our model on four tasks: word similarity, sentiment analysis, named entity recognition and part-of-speech tagging.