NATURAL LANGUAGE PROCESSING IN INFORMATION RETRIEVAL AND METADATA: A COMPREHENSIVE ANALYSIS OF MODERN APPROACHES AND APPLICATIONS

The integration of Natural Language Processing (NLP) with Information Retrieval (IR) systems has fundamentally transformed how machines understand, process and retrieve relevant information from vast data repositories.This research examines the evolution from traditional keyword-based retrieval methods to sophisticated neural approaches that leverage semantic understanding, contextual embeddings and structured metadata.Modern IR systems now employ advanced NLP techniques including semantic search, dense passage retrieval and knowledge graph integration to bridge the gap between user intent and document content.The analysis reveals that while neural models demonstrate superior performance in understanding context and handling vocabulary mismatches, they require substantial computational resources and training data.Metadata plays a crucial role in enhancing retrieval precision through structured data representation, faceted search capabilities and automated enrichment processes.Current evaluation frameworks utilizing metrics such as precision, recall and NDCG continue to evolve to address the complexities of neural ranking models and their behavioral characteristics.