Named Entity Recognition (NER) is a natural language processing (NLP) technique that identifies and categorizes key entities within large volumes of text.
The searched entities can extend among heterogeneous sets such as names of people, locations, organizations, dates, and military assets… This technology is crucial for intelligence analysis, helping defense agencies extract actionable information from reports, intercepted communications, and open-source intelligence. By automating the identification of critical entities, NER enhances data processing speed and accuracy, reducing the burden on analysts.
In military and defense contexts, NER aids in threat detection, battlefield situational awareness, and counterintelligence efforts. It can analyze vast amounts of unstructured data, such as intercepted messages or social media posts, to identify mentions of high-risk individuals, strategic locations, or planned operations. Additionally, NER helps streamline information retrieval in classified databases, ensuring fast access to relevant intelligence. By improving text analysis and decision-making, NER strengthens national security, surveillance, and cyber defense operations.
Project funded by the European Union under Grant Agreement No 101168083. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the European Commission can be held responsible for them.
