Singleton mentions, i.e.~entities mentioned only once in a text, are important to how humans understand discourse from a theoretical perspective. However previous attempts to incorporate their detection in end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention spans in the OntoNotes benchmark. This paper addresses this limitation by combining predicted mentions from existing nested NER systems and features derived from OntoNotes syntax trees. With this approach, we create a near approximation of the OntoNotes dataset with all singleton mentions, achieving ~94% recall on a sample of gold singletons. We then propose a two-step neural mention and coreference resolution system, named SPLICE, and compare its performance to the end-to-end approach in two scenarios: the OntoNotes test set and the out-of-domain (OOD) OntoGUM corpus. Results indicate that reconstructed singleton training yields results comparable to end-to-end systems for OntoNotes, while improving OOD stability (+1.1 avg. F1). We conduct error analysis for mention detection and delve into its impact on coreference clustering, revealing that precision improvements deliver more substantial benefits than increases in recall for resolving coreference chains.

通过将来自现有嵌套NER系统的预测提及与来自OntoNotes句法树的特征结合，我们在接近OntoNotes数据集中带有所有单例提及的情况下，实现了对黄金单例的94％召回率。然后，我们提出了一个名为SPLICE的两步神经提及和共指解析系统，并在OntoNotes测试集和域外OntoGUM语料库的两种情况下将其性能与端到端方法进行比较。结果表明，重构的单例训练在OntoNotes中产生了与端到端系统相当的结果，同时提高了域外稳定性（+1.1平均F1）。我们对提及检测进行了错误分析，并探讨其对共指聚类的影响，发现提高精度可为解决共指链提供更实质性的益处。

SPLICE：一种增强单例的核指代消解流程