Benchmarking Few-Shot Named Entity Recognition (FS-NER) for Real-World Applications: A Survey of Current State-of-the-Art and Future Directions

In recent years, there has been an increasing interest in developing few-shot learning systems for Named Entity Recognition (NER), particularly in domains where annotation processes require significant time, knowledge, and expertise. This survey aims to provide a comprehensive overview of the current state-of-the-art in FS-NER, highlighting its applications, techniques, limitations, and key aspects.

Introduction

Despite the rapid growth of available data, sharing private information remains a significant challenge in many domains, including healthcare, finance, and legal services. The manual effort required to build high-quality datasets further exacerbates this issue. As a result, finding alternative ways to train machine learning models with massive data has become essential.

FS-NER: A Fundamental Task for Many Downstream Applications

Named Entity Recognition is the task of identifying mentions of entities from unstructured text and classifying their type (e.g., person, organization, disease, drug). This fundamental task serves as a crucial step in many downstream applications, including question answering, dialogue systems, and knowledge graph construction. However, poor performance in NER could degrade the overall quality of these systems.

Current State-of-the-Art and Taxonomy

Our survey proposes a taxonomy that divides FS-NER algorithms into two macro-categories: model-centric and data-centric. By analyzing the underlying mechanisms of each category, we provide insights into the current state-of-the-art in few-shot NER research.

Domain Knowledge and Expertise in Few-Shot Learning

The need for FS-NER systems is particularly urgent in domains where annotation processes require time, knowledge, and expertise (e.g., healthcare, finance, legal). In these cases, the ability to train machine learning models with extremely limited available data becomes a crucial factor.

Recent Advances in Natural Language Processing (NLP)

The recent advancements in NLP have enabled significant performance improvements when large labeled training corpora are available. However, real-world applications often require the annotation of large amounts of documents to reach comparable results, which is highly expensive, time-consuming, and prone to human errors.

In this survey, we provide a comprehensive overview of the current state-of-the-art in FS-NER research, highlighting its applications, techniques, limitations, and key aspects. We believe that our taxonomy will facilitate future studies by providing a clear framework for categorizing FS-NER algorithms.

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