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。

从斯坦福和宾州树库到英语通用依存关系的多层注释转换

All Roads Lead to UD: Converting Stanford and Penn Parses to English  Universal Dependencies with Multilayer Annotations

Stanford typed dependencies are a widely desired representation of natural
language sentences, but parsing is one of the major computational bottlenecks
in text analysis systems. In light of the evolving definition of the Stanford
dependencies and developments in statistical dependency parsing algorithms,
this paper revisits the question of Cer et al. (2010): what is the tradeoff
between accuracy and speed in obtaining Stanford dependencies in particular? We
also explore the effects of input representations on this tradeoff:
part-of-speech tags, the novel use of an alternative dependency representation
as input, and distributional representaions of words. We find that direct
dependency parsing is a more viable solution than it was found to be in the
past. An accompanying software release can be found at:
this http URL

本文重新审视了 Cer 等人（2010）提出的一个问题：在获得 Stanford 依赖性方面，准确性与速度之间的权衡是什么？我们还探讨了输入表示对这种权衡的影响：词性标记、替代依赖性表示作为输入以及单词的分布表示。我们发现，直接依赖解析是一种比过去更可行的解决方案。