NER

Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique that involves identifying and classifying named entities within a given text. Named entities refer to specific objects, people, organizations, locations, and other types of entities that have a unique name.

NER systems use machine learning algorithms to identify and classify these named entities into different categories such as person, organization, location, date, time, currency, and more. The process involves training the system on a large corpus of labeled data to learn the patterns and features that distinguish one type of named entity from another.

One platform for creating better training data and debugging models.

Check it out on Github

What is NER used for?

The output of NER systems is a list of named entities identified in the text along with their corresponding entity type. NER is useful in any situation where a high-level overview of a large quantity of text is helpful. With NER, you can, at a glance, understand the subject or theme of a body of text and quickly group texts based on their relevancy or similarity. This technique is widely used in various applications such as information extraction, question answering, chatbots, sentiment analysis, and more. It helps to improve the accuracy of text analysis by providing a better understanding of the context and relationships between the entities in a given text.