From Syntax To Semantics: AI assisted Computational Linguistics In The Era Of Large computational Language Models

Computational linguistics has evolved from rule-based syntax analysis to deep semantic understanding, driven by advancements in artificial intelligence. The emergence of Large Language Models (LLMs) has revolutionized this field, offering new opportunities for AI-assisted linguistic processing. However, existing methods often struggle with ambiguity, contextual understanding, and resource-intensive training, limiting their ability to achieve human-like comprehension. To address these challenges, this study leverages LLMs to enhance syntactic parsing, semantic disambiguation, and context-aware language processing. Techniques such as prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) are employed to improve linguistic accuracy and efficiency. The proposed framework enables more precise translation, sentiment analysis, and information retrieval while reducing computational overhead. By integrating AI-driven computational linguistics with LLMs, this approach enhances natural language understanding and generation across diverse applications. The findings suggest that LLM-assisted models outperform traditional methods in terms of contextual coherence, accuracy, and adaptability, marking a significant advancement in the field.