Source:  
Abstract—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.

Q: What is the source about?