We describe and evaluate different approaches to the conversion of gold
standard corpus data from Stanford Typed Dependencies (SD) and Penn-style
constituent trees to the latest English Universal Dependencies representation
(UD 2.2). Our results indicate that pure SD to UD conversion is highly accurate
across multiple genres, resulting in around 1.5% errors, but can be improved
further to fewer than 0.5% errors given access to annotations beyond the pure
syntax tree, such as entity types and coreference resolution, which are
necessary for correct generation of several UD relations. We show that
constituent-based conversion using CoreNLP (with automatic NER) performs
substantially worse in all genres, including when using gold constituent trees,
primarily due to underspecification of phrasal grammatical functions.

本文介绍和评估了不同的方法将 Stanford Typed Dependencies (SD) 和 Penn-style 组分树的黄金标准语料库数据转化为最新的英语通用依赖关系 (UD2.2)。结果表明，多种语体下，纯 SD 到 UD 的转换具有高精度，只有 1.5% 的错误率，但如果访问纯语法树之外的注释，如实体类型和指代消解这样的注释，可以进一步提高精度，错误率可以降至不到 0.5%。我们表明，基于成分的转换使用 CoreNLP (自动命名实体识别) 在所有语体中表现不佳，包括在使用黄金成分树时，主要是由于短语语法功能的不充分规范化。