Anaphora and Coreference Resolution: A Review

TL;DR

This paper reviews entity resolution in NLP, focusing on anaphora and coreference resolution, analyzing datasets, metrics, and methods to clarify their scope and challenges in improving NLP tasks.

Contribution

It provides a comprehensive overview of anaphora and coreference resolution, clarifying their scope and analyzing research methods, datasets, and evaluation metrics.

Findings

Clarifies the scope of anaphora and coreference resolution.

Analyzes datasets and evaluation metrics used in the field.

Highlights research challenges and future directions.

Abstract

Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research. This field possesses immense potential to improve the performance of other NLP fields like machine translation, sentiment analysis, paraphrase detection, summarization, etc. The area of entity resolution in NLP has seen proliferation of research in two separate sub-areas namely: anaphora resolution and coreference resolution. Through this review article, we aim at clarifying the scope of these two tasks in entity resolution. We also carry out a detailed analysis of the datasets, evaluation metrics and research methods that have been adopted to tackle this NLP problem. This survey is motivated with the aim of providing the reader with a clear understanding of what constitutes this NLP problem and the issues that require attention.