# MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition

arXiv: https://arxiv.org/abs/2107.05418 · HTML: https://mlsanity.com/paper/2107.05418/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [Glyce: Glyph-vectors for Chinese Character Representations](https://mlsanity.com/paper/1901.10125/) (2019)
- [Simplify the Usage of Lexicon in Chinese NER](https://mlsanity.com/paper/1908.05969/) (2019)
- [F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media](https://mlsanity.com/paper/1611.04234/) (2016)
- [CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition](https://mlsanity.com/paper/1904.02141/) (2019)
- [Combining Discrete and Neural Features for Sequence Labeling](https://mlsanity.com/paper/1708.07279/) (2017)
- [Exploiting Multiple Embeddings for Chinese Named Entity Recognition](https://mlsanity.com/paper/1908.10657/) (2019)
- [Porous Lattice-based Transformer Encoder for Chinese NER](https://mlsanity.com/paper/1911.02733/) (2019)

## Built on by (most cited first, 11 of 11)

- [Boundary Smoothing for Named Entity Recognition](https://mlsanity.com/paper/2204.12031/) (2022)
- [MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention](https://mlsanity.com/paper/2112.01177/) (2021)
- [Deep Span Representations for Named Entity Recognition](https://mlsanity.com/paper/2210.04182/) (2022)
- [Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition](https://mlsanity.com/paper/2207.08191/) (2022)
- [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://mlsanity.com/paper/2302.08068/) (2023)
- [Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction](https://mlsanity.com/paper/2303.05082/) (2023)
- [NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2205.05832/) (2022)
- [Research on Named Entity Recognition in Improved transformer with R-Drop structure](https://mlsanity.com/paper/2306.08315/) (2023)
- [Unified Lattice Graph Fusion for Chinese Named Entity Recognition](https://mlsanity.com/paper/2312.16917/) (2023)
- [Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations](https://mlsanity.com/paper/2501.19093/) (2025)
- [CNM-BERT: A Drop-In Structural Embedding for Chinese Characters via Ideographic Description Sequences](https://mlsanity.com/paper/2608.05167/) (2026)

## Abstract (arXiv)

Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical information. Chinese characters have evolved from pictographs since ancient times, and their structure often reflects more information about the characters. This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters. Specifically, we use multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with the radical-level embedding. With the structural characteristics of Chinese characters, MECT can better capture the semantic information of Chinese characters for NER. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits and superiority of the proposed MECT method.footnoteThe source code of the proposed method is publicly available at https://github.com/CoderMusou/MECT4CNER.