Evolving Text Matching: A Systematic Review of Classical and Modern Approaches in the Neural Network

Writing matching has evolved dramatically from simple string comparison algorithms to sophisticated natural language processing techniques. This comprehensive literature review examines matching methods over the last 20 years, with special emphasis on transitioning from traditional frameworks to modern NLP methods to identify opportunities for practical theoretical integration and development exploring both models' fundamental principles, strengths and limitations. Our systematic review covers three main areas: (1) classical text matching algorithms, including Levenstein distance, Boyer-Moore, and Knuth-Morris-Pratt; (2) modern NLP techniques, such as transformer-based models and contextual ontologies; and (3) emerging hybrid approaches that seek to integrate these approaches. Intensive analysis of more than 40 papers from leading areas in information retrieval, natural language processing, and algorithmic evolution reveals key patterns in adopting text-matching strategies and highlights promising directions for future research. The study highlights a significant difference between the computational efficiency of traditional methods and the logical comprehension capabilities of modern NLP methods. Our study examines various attempts to bridge this gap and discusses the challenges and opportunities in integrating classical and modern approaches. We examine how different approaches manage the trade-off between computational complexity, logical clarity, and application-specific requirements.