  Recently, with the advances made in continuous representation of words (word
embeddings) and deep neural architectures, many research works are published in
the area of relation extraction and it is very difficult to keep track of so
many papers. To help future research, we present a comprehensive review of the
recently published research works in relation extraction. We mostly focus on
relation extraction using deep neural networks which have achieved
state-of-the-art performance on publicly available datasets. In this survey, we
cover sentence-level relation extraction to document-level relation extraction,
pipeline-based approaches to joint extraction approaches, annotated datasets to
distantly supervised datasets along with few very recent research directions
such as zero-shot or few-shot relation extraction, noise mitigation in
distantly supervised datasets. Regarding neural architectures, we cover
convolutional models, recurrent network models, attention network models, and
graph convolutional models in this survey.
