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

TL;DR

This paper introduces a novel framework combining knowledge enhancement and a span-based model to improve low-resource Chinese sequence labeling, achieving state-of-the-art results by mitigating semantic biases and enabling efficient nested entity extraction.

Contribution

It presents a new workflow with explanation prompts and a span-based model that together enhance understanding and extraction in low-resource, domain-specific Chinese sequence labeling tasks.

Findings

Achieves state-of-the-art performance on Chinese datasets.

Effectively mitigates semantic distribution biases.

Enables efficient nested entity extraction without external knowledge.

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.