Semantic Role Labeling: A Systematical Survey

2025-02-09

AI Summary

Semantic Role Labeling (SRL), a foundational NLP task, has long lacked a systematic, comprehensive survey. This paper addresses this gap by presenting the first holistic review of two decades of SRL research. Methodologically, we propose a unified four-dimensional taxonomy—covering model architectures, syntactic modeling paradigms, application scenarios, and multimodal extensions—and rigorously define the task’s boundaries. We systematically catalog benchmark datasets, evaluation metrics, and paradigm shifts in methodology and assessment. Furthermore, we analyze SRL’s evolving role and synergistic integration strategies in the era of large language models (LLMs). To support reproducibility and community advancement, we introduce and actively maintain Awesome-SRL, an open-source knowledge repository. Our contributions culminate in a structured knowledge graph spanning task definition, methodologies, evaluation frameworks, real-world applications, and emerging trends—providing both a principled reference and sustainable resources for future SRL research.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Semantic role labeling (SRL) is a central natural language processing (NLP) task aiming to understand the semantic roles within texts, facilitating a wide range of downstream applications. While SRL has garnered extensive and enduring research, there is currently a lack of a comprehensive survey that thoroughly organizes and synthesizes the field. This paper aims to review the entire research trajectory of the SRL community over the past two decades. We begin by providing a complete definition of SRL. To offer a comprehensive taxonomy, we categorize SRL methodologies into four key perspectives: model architectures, syntax feature modeling, application scenarios, and multi-modal extensions. Further, we discuss SRL benchmarks, evaluation metrics, and paradigm modeling approaches, while also exploring practical applications across various domains. Finally, we analyze future research directions in SRL, addressing the evolving role of SRL in the age of large language models (LLMs) and its potential impact on the broader NLP landscape. We maintain a public repository and consistently update related resources at: https://github.com/DreamH1gh/Awesome-SRL

Problem

• Comprehensive survey on Semantic Role Labeling (SRL).
• Categorizes SRL methodologies into four key perspectives.
• Explores future SRL research directions and LLMs impact.

Innovation

• Semantic Role Labeling taxonomy
• Multi-modal SRL extensions
• SRL benchmarks exploration