Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review

Fine-grained entity typing (FGET) is an important natural language processing (NLP) task. It is to assign fine-grained semantic types of a type taxonomy (e.g., Person/artist/actor) to entity mentions. Fine-grained entity semantic types have been successfully applied in many natural language processing applications, such as relation extraction, entity linking and question answering. The key challenge for FGET is how to deal with label noises that disperse in corpora since the corpora are normally automatically annotated. Various type taxonomies, typing methods and representation learning approaches for FGET have been proposed and developed in the past two decades. This paper systematically categorizes and reviews these various typing methods and representation learning approaches to provide a reference for future studies on FGET. We also present a comprehensive review of type taxonomies, resources, applications for FGET and methods for automatically generating FGET training corpora. Furthermore, we identify the current trends in FGET research and discuss future research directions for FGET. To the best of our knowledge, this is the first comprehensive review of FGET.