Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.
翻译:共同参考分辨率(CR)是自然语言处理中最具挑战性的领域之一。这一任务旨在确定所有与同一真实世界实体相关的文字引用。这一领域的研究分为共同参考分辨率和反亚光分辨率。由于在文本理解中的应用及其在信息提取系统、文件汇总和机器翻译等其他任务中的实用性,这个领域引起了相当大的兴趣。因此,它对这些系统的质量产生了重大影响。本条款回顾了该领域现有的公司和评估标准。然后,提供了从基于规则的方法到最新的深层学习技术的共同参考算法概览。最后,对波斯语中的共同引用分辨率和代名解析系统进行了调查。