A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition
This work addresses the challenge of extracting named entities with limited labeled examples, which is crucial for natural language processing applications, and represents an incremental improvement over existing contrastive learning methods.
The paper tackled the problem of insufficient distinguishability in context vector representation for few-shot named entity recognition by proposing a unified label-aware contrastive learning framework, which achieved an average absolute gain of 7% in micro F1 scores across various datasets.
Few-shot Named Entity Recognition (NER) aims to extract named entities using only a limited number of labeled examples. Existing contrastive learning methods often suffer from insufficient distinguishability in context vector representation because they either solely rely on label semantics or completely disregard them. To tackle this issue, we propose a unified label-aware token-level contrastive learning framework. Our approach enriches the context by utilizing label semantics as suffix prompts. Additionally, it simultaneously optimizes context-context and context-label contrastive learning objectives to enhance generalized discriminative contextual representations.Extensive experiments on various traditional test domains (OntoNotes, CoNLL'03, WNUT'17, GUM, I2B2) and the large-scale few-shot NER dataset (FEWNERD) demonstrate the effectiveness of our approach. It outperforms prior state-of-the-art models by a significant margin, achieving an average absolute gain of 7% in micro F1 scores across most scenarios. Further analysis reveals that our model benefits from its powerful transfer capability and improved contextual representations.