Mehr: A Persian Coreference Resolution Corpus | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 3، مهر 2023، صفحه 407-416 اصل مقاله (1.3 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.12641.2418 | ||
| نویسندگان | ||
| Hassan Haji Mohammadi1؛ Alireza Talebpour* 2؛ Ahamd Mahmoudi Aznaveh2؛ Samaneh Yazdani1 | ||
| 1Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| 2Department of computer engineering, Shahid Beheshti University, Tehran, Iran. | ||
| چکیده | ||
| Coreference resolution is one of the essential tasks of natural language processing. This task identifies all in-text expressions that refer to the same entity in the real world. Coreference resolution is used in other fields of natural language processing, such as information extraction, machine translation, and question-answering. This article presents a new coreference resolution corpus in Persian named Mehr corpus. The article's primary goal is to develop a Persian coreference corpus that resolves some of the previous Persian corpus's shortcomings while maintaining a high inter-annotator agreement. This corpus annotates coreference relations for noun phrases, named entities, pronouns, and nested named entities. Two baseline pronoun resolution systems are developed, and the results are reported. The corpus size includes 400 documents and about 170k tokens. Corpus annotation is done by WebAnno preprocessing tool. | ||
| کلیدواژهها | ||
| Natural Language Processing؛ Mention؛ Anaphora resolution؛ Antecedent | ||
| مراجع | ||
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