Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art

被引：0

作者：

Lu, Jing ^{[1
]}

Ng, Vincent ^{[1
]}

机构：

[1] Univ Texas Dallas, Human Language Technol Res Inst, Richardson, TX 75083 USA

来源：

PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP) | 2020年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Despite the significant progress on entity coreference resolution observed in recent years, there is a general lack of understanding of what has been improved. We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.

引用

页码：6620 / 6631

页数：12

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