解决英语部分性名词的语义角色标注问题，提升自然语言理解精度。
Semantic Role Labeling of NomBank Partitives
- 结合传统与Transformer模型，采用集成学习方法处理部分性名词
- 在宾格树库金标准解析下达到91.74%的F1分数
- 适合从事语义分析、句法语义接口研究的学者参考
本文针对诺姆银行语料库中英语部分性名词（如5%/REL of the price/ARG1；The price/ARG1 rose 5 percent/REL）的语义角色标注问题展开研究。采用了传统与基于Transformer的机器学习方法，并引入集成策略。最高性能系统在使用宾格树库金标准句法解析时，取得91.74%的F1值；使用伯克利神经解析器时，F1为91.12%。研究涵盖课堂与实验两种系统开发环境。
原文摘要 · Abstract (English)
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.
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