  Relation extraction (RE) involves identifying the relations between entities
from underlying content. RE serves as the foundation for many natural language
processing (NLP) and information retrieval applications, such as knowledge
graph completion and question answering. In recent years, deep neural networks
have dominated the field of RE and made noticeable progress. Subsequently, the
large pre-trained language models have taken the state-of-the-art RE to a new
level. This survey provides a comprehensive review of existing deep learning
techniques for RE. First, we introduce RE resources, including datasets and
evaluation metrics. Second, we propose a new taxonomy to categorize existing
works from three perspectives, i.e., text representation, context encoding, and
triplet prediction. Third, we discuss several important challenges faced by RE
and summarize potential techniques to tackle these challenges. Finally, we
outline some promising future directions and prospects in this field. This
survey is expected to facilitate researchers' collaborative efforts to address
the challenges of real-world RE systems.
