What is named entity recognition (NER) and how can it use?
Named entity recognition (NER) that sometimes referred to as entity chunking, extraction, or identification, is the task of identifying and categorizing key information (entities) in text. An entity can be any word or series of words that consistently refers to the same thing. Every entity is classified into a predetermined category. With named entity recognition, you can extract key information to comprehend what a text is about, or use it to collect important information to store in a database.
NER is a form of natural language processing (NLP), a subfield of artificial intelligence. NLP is concerned with analyzing natural language, i.e., any language that has developed naturally, rather than artificially, such as with computer coding languages.
NER is used in many fields in Natural Language Processing (NLP), and it can help answering many real-world questions, such as:
What is the importance of social network analysis?
Were specified products mentioned in comments?
Does the tweet contain the name of a person? Does the tweet contain this person’s location?