  Entity linking (EL) is the process of linking entity mentions appearing in
web text with their corresponding entities in a knowledge base. EL plays an
important role in the fields of knowledge engineering and data mining,
underlying a variety of downstream applications such as knowledge base
population, content analysis, relation extraction, and question answering. In
recent years, deep learning (DL), which has achieved tremendous success in
various domains, has also been leveraged in EL methods to surpass traditional
machine learning based methods and yield the state-of-the-art performance. In
this survey, we present a comprehensive review and analysis of existing DL
based EL methods. First of all, we propose a new taxonomy, which organizes
existing DL based EL methods using three axes: embedding, feature, and
algorithm. Then we systematically survey the representative EL methods along
the three axes of the taxonomy. Later, we introduce ten commonly used EL data
sets and give a quantitative performance analysis of DL based EL methods over
these data sets. Finally, we discuss the remaining limitations of existing
methods and highlight some promising future directions.
