Resolving Dates Using NLP

Abstract

In Natural Language Processing (NLP), accurately interpreting and resolving date references in unstructured text is essential for tasks like event tracking, data analysis, and document processing. This challenge stems from the variety of date formats and ambiguous expressions like “next Friday” or “two weeks ago.” A system has been developed that combines rule-based methods and machine learning techniques, using entity recognition, dependency parsing, and contextual analysis to resolve such ambiguities. Temporal normalization ensures consistent interpretation across various sources. The system enhances the accuracy of temporal data extraction, improving automation in fields like legal analysis, healthcare, finance, and news reporting. By integrating these techniques, the system effectively handles complex temporal expressions, adapting to different contexts and document types. This improves the reliability of the extracted dates, making automated data processing more precise and scalable.