Computational Approaches to Natural Language Understanding: Techniques for Semantic and Syntactic Representation

Abstract

Natural language understanding (NLU) is a central challenge in artificial intelligence, requiring computational systems to interpret linguistic structure, extract conceptual meaning, and infer relationships among words, phrases, and contexts. Modern approaches to NLU integrate machine learning, linguistic theory, symbolic representation, and representation learning to capture syntax, semantics, and discourse-level regularities. Despite progress, the inherent ambiguity, contextual variability, and compositional structure of human language continue to pose substantial challenges. This paper presents an extensive analysis of computational approaches to syntactic and semantic representation in NLU. Drawing exclusively on prior research from a broad corpus of artificial intelligence literature, we synthesize insights from thirty peer-reviewed works to form an interdisciplinary foundation for understanding linguistic modeling. These works span cognitive systems, machine ethics, robotics, decision-support systems, knowledge acquisition, autonomous systems, and probabilistic modeling. By aligning these perspectives with current trends in computational linguistics, we outline a conceptual framework for constructing robust NLU systems. The study develops a detailed account of symbolic, statistical, hybrid, and neural representation methods, and explains how they contribute to syntactic parsing, semantic composition, contextual reasoning, and meaning extraction.