A Brief History of Named Entity Recognition
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Abstract (arXiv)
A large amount of information in today's world is now stored in knowledge bases. Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity from raw text to insightful and structured knowledge bases. More concretely, it is identifying and classifying entities in the text that are crucial for Information Extraction, Semantic Annotation, Question Answering, Ontology Population, and so on. The process of NER has evolved in the last three decades since it first appeared in 1996. In this survey, we study the evolution of techniques employed for NER and compare the results, starting from supervised to the developing unsupervised learning methods.
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References · 6
- 2018Deep contextualized word representationscited by 2,285
- 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
- 2018A Survey on Deep Learning for Named Entity Recognitioncited by 281
- 2017Deep Active Learning for Named Entity Recognitioncited by 108
- 2019Cloze-driven Pretraining of Self-attention Networkscited by 41
- 2017NeuroNER: an easy-to-use program for named-entity recognition based on neural networkscited by 25
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