Abstract
This chapter provides a theoretical background of anaphora and introduces the varieties of this pervasive linguistic phenomenon. Next, it defines the task of anaphora resolution and introduces it as a three-stage process: identification of anaphors, location of the candidates for antecedents, and the resolution algorithm. After that, the chapter outlines a selection of influential and extensively cited anaphora resolution algorithms and proceeds to discuss issues related to the evaluation of anaphora resolution algorithms. Recent deep learning work on anaphora and coreference resolution is briefly presented as well. Finally, the chapter explains why anaphora resolution is important for various NLP applications.
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