  Coreference resolution (CR), identifying expressions referring to the same
real-world entity, is a fundamental challenge in natural language processing
(NLP). This paper explores the latest advancements in CR, spanning coreference
and anaphora resolution. We critically analyze the diverse corpora that have
fueled CR research, highlighting their strengths, limitations, and suitability
for various tasks. We examine the spectrum of evaluation metrics used to assess
CR systems, emphasizing their advantages, disadvantages, and the need for more
nuanced, task-specific metrics. Tracing the evolution of CR algorithms, we
provide a detailed overview of methodologies, from rule-based approaches to
cutting-edge deep learning architectures. We delve into mention-pair,
entity-based, cluster-ranking, sequence-to-sequence, and graph neural network
models, elucidating their theoretical foundations and performance on benchmark
datasets. Recognizing the unique challenges of Persian CR, we dedicate a
focused analysis to this under-resourced language. We examine existing Persian
CR systems and highlight the emergence of end-to-end neural models leveraging
pre-trained language models like ParsBERT. This review is an essential resource
for researchers and practitioners, offering a comprehensive overview of the
current state-of-the-art in CR, identifying key challenges, and charting a
course for future research in this rapidly evolving field.
