Named Entity Recognition (NER)
Named Entity Recognition (NER) is a natural language processing (NLP) technique used to automatically identify and classify named entities in text, such as people, locations, organizations, dates, or numerical values. The goal is to extract structured information from unstructured textual data.
NER has a wide range of applications: improving search engines through better indexing, extracting entities from legal or medical texts, enhancing sentiment analysis by detecting brand names, or powering question-answering systems. It is considered a cornerstone of modern NLP pipelines.
Approaches range from early rule-based and statistical models (like CRF or HMM) to state-of-the-art deep learning methods using transformer architectures such as BERT, RoBERTa, or spaCy, which have dramatically improved accuracy.
NER plays a key role in transforming raw text into structured knowledge. By recognizing entities, it enables downstream tasks like relation extraction, knowledge graph construction, and automated content tagging. For example, in finance, NER can extract company names and monetary values from reports; in healthcare, it helps identify diseases, treatments, and patient information in clinical notes.
Modern transformer-based NER systems go beyond simple dictionary lookups: they learn contextual embeddings, allowing the same word to be classified differently depending on its usage (e.g., “Amazon” as a company vs. a river). However, challenges remain: ambiguous entities, multilingual settings, and domain-specific jargon can reduce accuracy.
Researchers increasingly combine NER with active learning and human-in-the-loop validation, especially in sensitive domains like law and medicine, where mistakes can have significant consequences. In this sense, NER is not only a technical tool but also a bridge between unstructured text and structured decision-making.
References:
- Wikipedia – Named-entity recognition
- Stanford NLP – CRF-NER
- spaCy – NER Documentation
