Contrastive learning for nested Chinese Named Entity Recognition via template words

Existing Chinese named entity recognition (NER) research utilises 1D lexicon-based sequence labelling frameworks, which can only recognise flat entities. While lexicons serve as prior knowledge and enhance semantic information, they also pose completeness and resource requirements limitations. This paper proposes a template-based classification (TC) model to avoid lexicon issues and to identify nested entities. Template-based classification provides a template word for each entity type, which utilises contrastive learning to integrate the common characteristics among entities with the same category. Contrastive learning makes template words the centre points of their category in the vector space, thus improving generalisation ability. Additionally, TC presents a 2D table-filling label scheme that classifies entities based on the attention distribution of template words. The proposed novel decoder algorithm enables TC recognition of both flat and nested entities simultaneously. Experimental results show that TC achieves the state-of-the-art performance on five Chinese datasets.