  The availability of large amounts of computer-readable textual data and
hardware that can process the data has shifted the focus of knowledge projects
towards deep learning architecture. Natural Language Processing, particularly
the task of Named Entity Recognition is no exception. The bulk of the learning
methods that have produced state-of-the-art results have changed the deep
learning model, the training method used, the training data itself or the
encoding of the output of the NER system. In this paper, we review significant
learning methods that have been employed for NER in the recent past and how
they came about from the linear learning methods of the past. We also cover the
progress of related tasks that are upstream or downstream to NER, e.g.,
sequence tagging, entity linking, etc., wherever the processes in question have
also improved NER results.
