This article is about Semantic Role Labeling for English partitive nouns (5%/REL of the price/ARG1; The price/ARG1 rose 5 percent/REL) in the NomBank annotated corpus. Several systems are described using traditional and transformer-based machine learning, as well as ensembling. Our highest scoring system achieves an F1 of 91.74% using "gold" parses from the Penn Treebank and 91.12% when using the Berkeley Neural parser. This research includes both classroom and experimental settings for system development.

翻译：本文研究针对NomBank标注语料库中英语部分格名词（如"5%/REL of the price/ARG1"；"The price/ARG1 rose 5 percent/REL"）的语义角色标注。我们描述了基于传统机器学习、Transformer架构及集成学习的多种系统。在使用宾州树库"黄金"句法分析时，最优系统F1值达到91.74%；使用伯克利神经句法分析器时达到91.12%。本研究涵盖了系统开发的课堂教学环境与实验环境双重场景。