Named Entity Recognition (NER)

Named Entity Recognition (NER) is a form of natural language processing (NLP) used to identify and classify named entities in a text, such as people, places, organizations, and more. NER is an essential part of text analytics and helps to extract and recognize entities from unstructured text.

NER is used to organize large amounts of unstructured data into more structured forms and improve the accuracy of search engines, question-answering systems, and text classification for applications like document summaries. This provides a powerful tool for data scientists and machine learning developers to quickly and accurately process and distinguish between entities like organizations, locations, people, products, and more.

NER can be used to analyze customer feedback to identify key themes, extract data from documents and webpages for use in further analysis, and help automate customer experience management tasks.

As the demand for better search, question-answering, and natural language understanding capabilities grows, the use of NLP in the form of NER is expected to be increasingly adopted across various domains. NER can improve the accuracy and speed of tasks and solve many real-world problems in data science and machine learning.