Shahrood University of Technology Journal of AI and Data Mining 2322-5211 11 3 2023 07 01 Mehr: A Persian Coreference Resolution Corpus 407 416 2897 10.22044/jadm.2023.12641.2418 EN Hassan Haji Mohammadi Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran. Alireza Talebpour Department of computer engineering, Shahid Beheshti University, Tehran, Iran. Ahamd Mahmoudi Aznaveh Department of computer engineering, Shahid Beheshti University, Tehran, Iran. Samaneh Yazdani Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran. Journal Article 2023 02 12 Coreference resolution is one of the essential tasks of natural language<br />processing. This task identifies all in-text expressions that refer to the<br />same entity in the real world. Coreference resolution is used in other<br />fields of natural language processing, such as information extraction,<br />machine translation, and question-answering.<br />This article presents a new coreference resolution corpus in Persian<br />named Mehr corpus. The article's primary goal is to develop a Persian<br />coreference corpus that resolves some of the previous Persian corpus's<br />shortcomings while maintaining a high inter-annotator agreement. This<br />corpus annotates coreference relations for noun phrases, named<br />entities, pronouns, and nested named entities. Two baseline pronoun<br />resolution systems are developed, and the results are reported. The<br />corpus size includes 400 documents and about 170k tokens. Corpus<br />annotation is done by WebAnno preprocessing tool. https://jad.shahroodut.ac.ir/article_2897_1b4eef782cd783c3876250545dff45ed.pdf