Entity Linking Meets Deep Learning: Techniques and Solutions

• URL: http://arxiv.org/abs/2109.12520v1
• Date: Sun, 26 Sep 2021 07:57:38 GMT
• Title: Entity Linking Meets Deep Learning: Techniques and Solutions
• Authors: Wei Shen, Yuhan Li, Yinan Liu, Jiawei Han, Jianyong Wang, Xiaojie Yuan
• Abstract summary: We present a comprehensive review and analysis of existing deep learning based EL methods. We propose a new taxonomy, which organizes existing DL based EL methods using three axes: embedding, feature, and algorithm. We give a quantitative performance analysis of DL based EL methods over data sets.
• Score: 49.017379833990155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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