# pyBART: Evidence-based Syntactic Transformations for IE

arXiv: https://arxiv.org/abs/2005.01306 · HTML: https://mlsanity.com/paper/2005.01306/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 5 of 5)

- [Automatic extraction of materials and properties from superconductors scientific literature](https://mlsanity.com/paper/2210.15600/) (2022)
- [On the Relation between Syntactic Divergence and Zero-Shot Performance](https://mlsanity.com/paper/2110.04644/) (2021)
- [Two Kinds of Recall](https://mlsanity.com/paper/2303.10527/) (2023)
- [The Perfect Victim: Computational Analysis of Judicial Attitudes towards Victims of Sexual Violence](https://mlsanity.com/paper/2305.05302/) (2023)
- [Unsupervised Mapping of Arguments of Deverbal Nouns to Their Corresponding Verbal Labels](https://mlsanity.com/paper/2306.13922/) (2023)

## Abstract (arXiv)

Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications. Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs. However, they are not available to Python users, and are also limited in coverage. We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation. The library can work as a standalone package or be integrated within a spaCy NLP pipeline. When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns.