  Knowledge is a formal way of understanding the world, providing a human-level
cognition and intelligence for the next-generation artificial intelligence
(AI). One of the representations of knowledge is semantic relations between
entities. An effective way to automatically acquire this important knowledge,
called Relation Extraction (RE), a sub-task of information extraction, plays a
vital role in Natural Language Processing (NLP). Its purpose is to identify
semantic relations between entities from natural language text. To date, there
are several studies for RE in previous works, which have documented these
techniques based on Deep Neural Networks (DNNs) become a prevailing technique
in this research. Especially, the supervised and distant supervision methods
based on DNNs are the most popular and reliable solutions for RE. This article
1) introduces some general concepts, and further 2) gives a comprehensive
overview of DNNs in RE from two points of view: supervised RE, which attempts
to improve the standard RE systems, and distant supervision RE, which adopts
DNNs to design sentence encoder and de-noise method. We further 3) cover some
novel methods and recent trends as well as discuss possible future research
directions for this task.
