Foundations and Methodologies of Rule-Based Semantic Role Labelling: A Review

Abstract

Semantic Role Labeling (SRL) provides a structured semantic interpretation of natural language by identifying predicate–argument relationships that encode event-level information such as agentivity, affected entities, location, and temporality. As a foundational layer in the NLP pipeline, SRL leverages syntactic features from part-of-speech tagging, chunking, and dependency parsing to generate semantic representations that support downstream tasks including question answering, information extraction, and machine translation. This review presents a detailed technical analysis of traditional rule-based SRL, which operationalizes linguistic theory through deterministic mappings between syntactic configurations and semantic roles. The methodology encompasses sentence segmentation, tokenization, POS tagging, lemmatization, predicate detection, syntactic dependency analysis, rule application, and role validation. Semantic roles are assigned through handcrafted patterns derived from grammatical relations such as nsubj, obj, iobj, and specific prepositional structures. While rule-based SRL ensures interpretability, structural consistency, and independence from annotated corpora, its reliance on manually engineered rules restricts generalization to diverse or ambiguous linguistic inputs. This paper rigorously evaluates these mechanisms, highlighting the architectural characteristics, strengths, and inherent limitations that define rule-based SRL within modern computational linguistics.