  Sequence labeling (SL) is a fundamental research problem encompassing a
variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition
(NER), text chunking, etc. Though prevalent and effective in many downstream
applications (e.g., information retrieval, question answering, and knowledge
graph embedding), conventional sequence labeling approaches heavily rely on
hand-crafted or language-specific features. Recently, deep learning has been
employed for sequence labeling tasks due to its powerful capability in
automatically learning complex features of instances and effectively yielding
the stat-of-the-art performances. In this paper, we aim to present a
comprehensive review of existing deep learning-based sequence labeling models,
which consists of three related tasks, e.g., part-of-speech tagging, named
entity recognition, and text chunking. Then, we systematically present the
existing approaches base on a scientific taxonomy, as well as the widely-used
experimental datasets and popularly-adopted evaluation metrics in the SL
domain. Furthermore, we also present an in-depth analysis of different SL
models on the factors that may affect the performance and future directions in
the SL domain.
