Abstract :

Named Entity Recognition (NER) is an essential task in Natural Language Processing that focuses on identifying and classifying key information such as names of people, organizations, locations, dates, and other predefined entities within text data. This project aims to develop an efficient NER system using machine learning techniques to extract meaningful information from unstructured textual datasets. The system enhances text understanding by converting raw textual inputs into structured knowledge useful for applications like information retrieval, question answering, and text summarization. Machine learning algorithms such as Conditional Random Fields, Support Vector Machines, and neural network-based architectures are explored for entity classification. The proposed model is trained using annotated datasets to achieve high accuracy and robustness across different text forms. Performance is evaluated based on precision, recall, and F1-score to ensure reliability. The study demonstrat