Taxonomy, Benchmarks and Computational Approaches for Semantic Error Correction in Natural Language Processing

Abstract: While modern Natural Language Processing (NLP) systems excel at finding basic grammar and spelling mistakes, identifying and correcting semantic errors (problem in the meaning of the text) remains an intricate challenge. Unlike grammar faults, semantic errors often use correct spelling and rules, but they break context, disrupt logical flow, or misuse words. This paper provides a structured investigation into semantic error detection and correction. We present a novel, fine-grained taxonomy that divides semantic errors into contextual mismatches and wrong word choices. We then evaluate standard datasets to highlight their lack of semantic coverage, compare traditional rule-based methods with modern deep learning models like Transformers and analyse system performance metrics across standard evaluation dimensions, identifying critical bottlenecks and outlining strategic trajectories for future NLP architectures.

Keywords: Semantic Error Correction, Contextual Error Detection, Distributional Semantics, Lexical Collocation, Neural, Machine Translation, Language Modelling.