# Unified Lattice Graph Fusion for Chinese Named Entity Recognition

arXiv: https://arxiv.org/abs/2312.16917 · HTML: https://mlsanity.com/paper/2312.16917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 15 of 15)

- [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)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Relation-Aware Graph Attention Network for Visual Question Answering](https://mlsanity.com/paper/1903.12314/) (2019)
- [Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering](https://mlsanity.com/paper/1812.05252/) (2018)
- [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)
- [A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://mlsanity.com/paper/2007.08742/) (2020)
- [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021)
- [Learning Sparse Sharing Architectures for Multiple Tasks](https://mlsanity.com/paper/1911.05034/) (2019)
- [GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering](https://mlsanity.com/paper/2104.10283/) (2021)
- [F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media](https://mlsanity.com/paper/1611.04234/) (2016)
- [MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2107.05418/) (2021)
- [Combining Discrete and Neural Features for Sequence Labeling](https://mlsanity.com/paper/1708.07279/) (2017)

## Abstract (arXiv)

Integrating lexicon into character-level sequence has been proven effective to leverage word boundary and semantic information in Chinese named entity recognition (NER). However, prior approaches usually utilize feature weighting and position coupling to integrate word information, but ignore the semantic and contextual correspondence between the fine-grained semantic units in the character-word space. To solve this issue, we propose a Unified Lattice Graph Fusion (ULGF) approach for Chinese NER. ULGF can explicitly capture various semantic and boundary relations across different semantic units with the adjacency matrix by converting the lattice structure into a unified graph. We stack multiple graph-based intra-source self-attention and inter-source cross-gating fusion layers that iteratively carry out semantic interactions to learn node representations. To alleviate the over-reliance on word information, we further propose to leverage lexicon entity classification as an auxiliary task. Experiments on four Chinese NER benchmark datasets demonstrate the superiority of our ULGF approach.