A survey on recent advances in named entity recognition

Named Entity Recognition (NER) is a field of computer science and natural language processing (NLP) that deals with the identification and classification of named items in unstructured text. The items in question belong to predefined semantic types such as persons, locations, and organizations [Grishman and Sundheim, 1996a]. NER is today a key component in areas including machine translation [Babych and Hartley, 2003], question-answering [Mollá et al., 2006], and information retrieval [Guo et al., 2009]. A number of NER systems have been developed, particularly for English, but also for other languages, including Chinese [Liu et al., 2022] and French [Mikheev et al., 1999]. Early NER systems used algorithms based on handcrafted rules, lexicons, and spelling features [Rau, 1991]. Systems were subsequently developed that used algorithms based on machine learning [Nadeau and Sekine, 2007], neural networks [Collobert, 2011], and transformers [Labusch et al., 2019a].