NLP: Named Entity Recognition
This article delves into the fascinating world of Named Entity Recognition (NER), a fundamental technique in Natural Language Processing (NLP) that has undergone a remarkable evolution over the years. NER is the art of automatically identifying and categorizing specific entities within text, such as names of people, places, organizations, dates, and more, into predefined labels or categories. It plays a pivotal role in extracting valuable information from unstructured textual data. In the early days, classical methodologies relied on rule-based systems and manual annotation, while today’s NER systems have been revolutionized by cutting-edge transformer models like BERT. In this article, we will explore the journey from classical NER approaches to the modern methodologies powered by deep learning, shedding light on how these techniques are employed and their significance in various domains.
