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

arXiv: https://arxiv.org/abs/1909.00522 · HTML: https://mlsanity.com/paper/1909.00522/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://mlsanity.com/paper/2306.01966/) (2023)
- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://mlsanity.com/paper/2403.13560/) (2024)
- [A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing](https://mlsanity.com/paper/2210.07873/) (2022)
- [Mischievous Nominal Constructions in Universal Dependencies](https://mlsanity.com/paper/2108.12928/) (2021)
- [Hybrid Human-LLM Corpus Construction and LLM Evaluation for Rare Linguistic Phenomena](https://mlsanity.com/paper/2403.06965/) (2024)
- [Are UD Treebanks Getting More Consistent? A Report Card for English UD](https://mlsanity.com/paper/2302.00636/) (2023)
- [Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech](https://mlsanity.com/paper/2109.10952/) (2021)
- [Minor changes make a difference: a case study on the consistency of UD-based dependency parsers](https://mlsanity.com/paper/2111.15413/) (2021)
- [Syntax as a Rosetta Stone: Universal Dependencies for In-Context Coptic Translation](https://mlsanity.com/paper/2604.18758/) (2026)

## Abstract (arXiv)

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.