  Named entity recognition (NER) is the task to identify mentions of rigid
designators from text belonging to predefined semantic types such as person,
location, organization etc. NER always serves as the foundation for many
natural language applications such as question answering, text summarization,
and machine translation. Early NER systems got a huge success in achieving good
performance with the cost of human engineering in designing domain-specific
features and rules. In recent years, deep learning, empowered by continuous
real-valued vector representations and semantic composition through nonlinear
processing, has been employed in NER systems, yielding stat-of-the-art
performance. In this paper, we provide a comprehensive review on existing deep
learning techniques for NER. We first introduce NER resources, including tagged
NER corpora and off-the-shelf NER tools. Then, we systematically categorize
existing works based on a taxonomy along three axes: distributed
representations for input, context encoder, and tag decoder. Next, we survey
the most representative methods for recent applied techniques of deep learning
in new NER problem settings and applications. Finally, we present readers with
the challenges faced by NER systems and outline future directions in this area.
