A complete guide to Named Entity Recognition (NER) in 2025

Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique used to identify mentions of essential entities such as people, organizations, and locations within text-based data. The process, which originated at the Message Understanding Conference (MUC-6), has become crucial in automating information extraction across various sectors. NER is employed in support chatbots to automatically handle customer interactions, in medical fields to mine valuable information from biomedical literature, and in document categorization to efficiently organize large datasets. Building a highly accurate NER model necessitates a deep understanding of machine learning and mathematics, often utilizing frameworks like PyTorch and TensorFlow with pre-trained models such as BERT, which helps in fine-tuning and adapting models to specific tasks. Popular libraries such as NLTK and Spacy are frequently used to implement NER, showcasing its practical applications in business processes like automating the extraction and analysis of data from invoices and receipts through a combination of Optical Character Recognition (OCR) and deep learning.