Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations

2025-01-31

AI Summary

Sequence labeling for Chinese low-resource domains—such as named entity recognition (NER)—suffers from weak contextual understanding and poor performance on nested entities. Method: This paper proposes KnowFREE, a span-based, label-explanation-driven framework that requires no external knowledge. It integrates context-sensitive label explanations generated by large language models (LLMs) and introduces a lightweight knowledge fusion mechanism to jointly correct label semantic biases and extract nested entities. We further design an explanation-driven knowledge enhancement workflow and an extended label feature modeling strategy. Contribution/Results: KnowFREE achieves state-of-the-art (SOTA) performance on multiple Chinese low-resource NER benchmarks, with significant F1-score improvements. Notably, it demonstrates strong robustness in handling nested structures and sparsely annotated instances, validating its effectiveness in challenging low-resource scenarios.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Sequence labeling remains a significant challenge in low-resource, domain-specific scenarios, particularly for character-dense languages like Chinese. Existing methods primarily focus on enhancing model comprehension and improving data diversity to boost performance. However, these approaches still struggle with inadequate model applicability and semantic distribution biases in domain-specific contexts. To overcome these limitations, we propose a novel framework that combines an LLM-based knowledge enhancement workflow with a span-based Knowledge Fusion for Rich and Efficient Extraction (KnowFREE) model. Our workflow employs explanation prompts to generate precise contextual interpretations of target entities, effectively mitigating semantic biases and enriching the model's contextual understanding. The KnowFREE model further integrates extension label features, enabling efficient nested entity extraction without relying on external knowledge during inference. Experiments on multiple Chinese domain-specific sequence labeling datasets demonstrate that our approach achieves state-of-the-art performance, effectively addressing the challenges posed by low-resource settings.

Problem

• Limited Resources
• Domain-specific Sequence Labeling
• Context Understanding

Innovation

• Large Model Enhancement
• KnowFREE Model Integration
• Domain-specific Lexical Understanding