A FRAMEWORK FOR INTELLIGENT TEXT CORRECTION IN AUTOMOTIVE TECHNICAL DOCUMENTATION USING NLP AND POS TAGGING

Abstract

Text correction systems in automotive technical documentation face unique challenges due to domain-specific terminology, complex abbreviations, and context-dependent meanings. This article presents an advanced approach to enhancing text accuracy in automotive documentation through Natural Language Processing (NLP) and Part-of-Speech (POS) tagging techniques. The methodology integrates domain-specific language models with POS-based error detection to identify and correct typographical errors, grammatical inconsistencies, and terminology mismatches in automotive technical content. This article demonstrates significant improvements over traditional correction methods in handling automotive-specific terminology and context, particularly in diagnostic logs, technical manuals, and customer documentation. Experimental results show marked enhancement in both error detection precision and correction quality compared to general-purpose NLP tools, suggesting promising applications for improving technical communication accuracy in the automotive industry. The proposed framework provides a foundation for developing more robust, domain-aware text correction systems, with potential applications in real-time correction and multilingual support for global automotive markets.