Named Entity Recognition

Named Entity Recognition (NER) is one of the popular Natural Language Processing techniques that allows automatic identification and classification of important entities within unstructured text. The method allows computers to find specific words or phrases representing real-world objects like people, organizations, locations, dates, products, companies, currencies, and various events. In the example sentence "GeoWGS84 provides GIS services in India," an NER system would recognize "GeoWGS84" as an organization and "India" as a location.

Named Entity Recognition is done using the context and linguistic patterns of words analyzed using machine learning, deep learning, or transformer-based algorithms. Modern NER systems can work with complicated language constructions and identify entities based on the meaning of the surrounding words. Many NLP libraries and frameworks, including spaCy, Hugging Face Transformers, and NLTK, have solutions allowing to implement NER applications.

Named Entity Recognition can be applied in numerous industries. This method is widely used by search engines to increase query understanding, customer support systems to get additional information, document processing software to find the required details, financial systems to identify companies and monetary values, healthcare solutions to extract the required information from clinical documents, and GIS applications to find geographic locations.

Named Entity Recognition allows converting unstructured text into structured data, making it easier to search, analyze, sort, and use further within different systems.