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.

Rahul S

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Named Entity Recognition (NER) is a fundamental technique in Natural Language Processing (NLP). It plays a crucial role in information extraction from unstructured text data.

It is about automatically identifying and classifying specific entities within a text, such as names of people, locations, organizations, dates, and more, into predefined categories or labels.

NER has evolved significantly over the years — transitioning from classical methodologies to the cutting-edge transformer models, such as BERT, which have revolutionized the field of NLP.

Classical Methodology:

In the early days of NER, classical methodologies relied on rule-based systems and heuristics to identify entities in text. These systems used handcrafted rules and dictionaries to recognize entities like names of…

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