Named entity recognition (NER) is a semantic information extraction technique that locates and categorizes relevant entity data from text at scale.
Named entities include everything from names and brands to addresses, locations, and virtually any other form of classifiable textual information. Named entity extraction makes these specific entities usable to NLP models.
NER for machine learning is essential in a wide range of commercial and non-commercial industries, such as social media analytics, customer services, translation, biomedicine, the natural sciences, cybersecurity, and the news and media. Once trained, named entity detection algorithms derive meaning from text with minimal manual processing, which is vital for sentiment analysis, text analytics, and other NLP purposes.
Named entity extraction in NLP equips models with knowledge of how various entities interlink and cross-match between passages. This is vital for bringing coherency to NLP models that frequently parse entity-rich texts.
Aya Data has proven expertise in applying named entity recognition annotation in both commercial and non-commercial contexts. Our team will label each named entity accurately and apply relationship extraction to link entities throughout the text.
Our NER services will help you extract the insights you need to achieve your business goals, respond to complex customer queries, build reputational management workflows, or train any other applications that work with unstructured textual data.