Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art
Jing Lu, Vincent Ng
2020Year
13Citations
4Top-tier citations
Abstract
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.
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Install the CLIlune papers fulltext c375bef2-fe7f-4a3f-bf4d-8d3b0c1517e2Cited by top-tier papers4
- Incorporating Constituent Syntax for Coreference ResolutionFan Jiang, Trevor CohnAAAI 2022 · 6 citations
- Major Entity Identification: A Generalizable Alternative to Coreference ResolutionKawshik Sundar, Shubham Toshniwal, Makarand Tapaswi, Vineet GandhiEMNLP 2024 · 4 citations
- Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference ResolutionNupoor Gandhi, Anjalie Field, Emma StrubellACL 2023 · 3 citations
- Entity-Based Knowledge Conflicts in Question AnsweringShayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh et al.EMNLP 2021 · 3 citations
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