  Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have
been studied and developed widely for decades, but accurate systems using deep
neural networks (NN) have only been introduced in the last few years. We
present a comprehensive survey of deep neural network architectures for NER,
and contrast them with previous approaches to NER based on feature engineering
and other supervised or semi-supervised learning algorithms. Our results
highlight the improvements achieved by neural networks, and show how
incorporating some of the lessons learned from past work on feature-based NER
systems can yield further improvements.
