CRENER: A Character Relation Enhanced Chinese NER Model

TL;DR

CRENER introduces a novel Chinese NER model that leverages character relationships and an enhanced transformer encoder to improve entity boundary detection and overall accuracy, outperforming existing methods.

Contribution

The paper proposes a character relation enhanced NER model with a fine-grained relationship classification approach and a specialized transformer encoder for better Chinese NER performance.

Findings

Outperforms state-of-the-art baselines on four Chinese NER datasets

Effective modeling of character relationships improves boundary recognition

Ablation studies confirm the model's components contribute to accuracy

Abstract

Chinese Named Entity Recognition (NER) is an important task in information extraction, which has a significant impact on downstream applications. Due to the lack of natural separators in Chinese, previous NER methods mostly relied on external dictionaries to enrich the semantic and boundary information of Chinese words. However, such methods may introduce noise that affects the accuracy of named entity recognition. To this end, we propose a character relation enhanced Chinese NER model (CRENER). This model defines four types of tags that reflect the relationships between characters, and proposes a fine-grained modeling of the relationships between characters based on three types of relationships: adjacency relations between characters, relations between characters and tags, and relations between tags, to more accurately identify entity boundaries and improve Chinese NER accuracy. Specifically, we transform the Chinese NER task into a character-character relationship classification task, ensuring the accuracy of entity boundary recognition through joint modeling of relation tags. To enhance the model's ability to understand contextual information, WRENER further constructed an adapted transformer encoder that combines unscaled direction-aware and distance-aware masked self-attention mechanisms. Moreover, a relationship representation enhancement module was constructed to model predefined relationship tags, effectively mining the relationship representations between characters and tags. Experiments conducted on four well-known Chinese NER benchmark datasets have shown that the proposed model outperforms state-of-the-art baselines. The ablation experiment also demonstrated the effectiveness of the proposed model.