arXiv:2502.08660v4 Announce Type: replace Abstract: Semantic role labeling (SRL) is a central natural language processing task for understanding predicate-argument structures within texts and enabling downstream applications. Despite extensive research, comprehensive surveys that critically synthesize the field from a unified perspective remain lacking. This survey makes several contributions beyond organizing existing work. We propose a unified four-dimensional taxonomy that categorizes SRL research along model architectures, syntax feature modeling, application scenarios, and multimodal exte
The proliferation of pretrained language models necessitates a systematic survey to consolidate advancements and identify future research directions in Semantic Role Labeling.
Improved semantic role labeling (SRL) is fundamental for advancing general AI capabilities, enabling more sophisticated natural language understanding in AI systems and agents.
A unified taxonomy for SRL research will provide a clearer framework for development, potentially accelerating progress in AI applications requiring deep language comprehension.
- · AI researchers
- · NLP developers
- · AI-driven software platforms
- · AI systems with poor natural language understanding
The survey provides a consolidated view of SRL, guiding future research and development in language models.
Enhanced SRL capabilities will lead to more robust and accurate AI agents capable of understanding and executing complex instructions.
Improved AI language understanding could accelerate the development of autonomous AI systems, impacting various industries and white-collar workflows.
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