Entity Extraction
Entity extraction, also known as named entity recognition (NER), is a technique within Natural Language Processing (NLP) to identify and extract specific information in text. The purpose of entity extraction is to recognize and label specific entities, such as names of people, locations, organizations, dates and companies.
How does entity extraction work?
Entity extraction uses algorithms to recognize and categorize patterns in text. These algorithms use rules and machine learning to develop a model that can recognize and extract entities from text.
In the machine learning process, the model is trained on examples of tagged text, where the entities are indicated. The model then learns to recognize and use these patterns to extract entities from new unknown text. The process of entity extraction is of great importance in many applications, including analyzing social media, tracking financial data, and automating customer service. It makes it possible to quickly and accurately find information in large amounts of text and therefore can be an important tool for companies and organizations working with large amounts of textual data.
