Hybrid Linguistic Models: Combining Traditional Philology and Deep Learning for Historical Analysis

Traditional philology, with its rigorous linguistic analysis of historical texts, and deep learning, with its advanced computational capabilities, offer complementary strengths for historical language analysis. However, existing methods often struggle with inaccuracies in text reconstruction, semantic ambiguity, and limited adaptability to diverse linguistic structures. To address these challenges, we propose the Hybrid Linguistic Model for Historical Analysis (HLM-HA), which integrates rule-based philological approaches with deep neural networks. HLM-HA leverages linguistic rules to enhance model training, ensuring contextual accuracy while utilizing deep learning’s capacity to analyze vast textual datasets. Using HLM-HA, historical texts are processed through a dual-layered framework: first, traditional philological techniques refine linguistic input, and second, deep learning models (DLM) optimize pattern recognition and semantic interpretation. This hybrid lingusitic significantly improves text reconstruction, contextual inference, and historical language modeling. Our findings indicate that HLM-HA outperforms existing methods in historical text accuracy, semantic clarity, and adaptability to complex linguistic structures. The proposed framework enhances the precision of historical linguistic studies, paving the way for more reliable text analysis in philology and computational linguistics.