# UniParse: A universal graph-based parsing toolkit

arXiv: https://arxiv.org/abs/1807.04053 · HTML: https://mlsanity.com/paper/1807.04053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Data-driven Parsing Evaluation for Child-Parent Interactions](https://mlsanity.com/paper/2209.13778/) (2022)

## Abstract (arXiv)

This paper describes the design and use of the graph-based parsing framework and toolkit UniParse, released as an open-source python software package. UniParse as a framework novelly streamlines research prototyping, development and evaluation of graph-based dependency parsing architectures. UniParse does this by enabling highly efficient, sufficiently independent, easily readable, and easily extensible implementations for all dependency parser components. We distribute the toolkit with ready-made configurations as re-implementations of all current state-of-the-art first-order graph-based parsers, including even more efficient Cython implementations of both encoders and decoders, as well as the required specialised loss functions.