A Quarter‐Century of Analogical Natural Language Processing: A Systematic Review

ABSTRACT

Analogical reasoning constitutes a foundational pillar of human cognition, enabling the seamless extrapolation of knowledge from known structural precedents to completely novel contexts. Within Computational Linguistics, the operationalization of relational proportions () has emerged as a robust, knowledge‐light paradigm for learning, inference, and data‐driven generalization. This paper presents a comprehensive Systematic Literature Review (SLR) tracking the architectural taxonomy, structural transitions, and empirical milestones of analogical Natural Language Processing (NLP) over the past quarter‐century (2000–2025). Adhering strictly to standardized PRISMA guidelines, we aggregate and synthesize 43 primary studies across four distinct evolutionary eras: discrete symbolic/algebraic frameworks, distributional pattern induction models, static embedding offset topologies, and deep contextualized transformer architectures. Each paradigm is critically evaluated in terms of mathematical formulations, dataset configurations, optimization metrics, and downstream operational limitations across core language processing tasks, including morphology, machine translation, and sentence semantic alignment. Methodologically, we reconcile qualitative evidence with quantitative benchmark syntheses, formalize axiomatic constraints across sentential domains, and assess potential publication bias. Our cross‐paradigm synthesis exposes critical trade‐offs between symbolic interpretability, vocabulary coverage limits, and continuous vector space distortions. Finally, we establish a forward‐looking research agenda mapping current open problems—specifically focusing on text morphing generation, analogical dataset augmentation, personalized preference learning, and explainable neuro‐symbolic interface integration—to guide future developments in data‐efficient and interpretable natural language architectures.