# Hierarchical Bracketing Encodings for Dependency Parsing as Tagging

arXiv: https://arxiv.org/abs/2505.11693 · HTML: https://mlsanity.com/paper/2505.11693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Viable Dependency Parsing as Sequence Labeling](https://mlsanity.com/paper/1902.10505/) (2019)
- [Bracketing Encodings for 2-Planar Dependency Parsing](https://mlsanity.com/paper/2011.00596/) (2020)
- [A Unifying Theory of Transition-based and Sequence Labeling Parsing](https://mlsanity.com/paper/2011.00584/) (2020)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing](https://mlsanity.com/paper/1706.03357/) (2017)

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

- [Hierarchical Bracketing Encodings Work for Dependency Graphs](https://mlsanity.com/paper/2509.09388/) (2025)

## Abstract (arXiv)

We present a family of encodings for sequence labeling dependency parsing, based on the concept of hierarchical bracketing. We prove that the existing 4-bit projective encoding belongs to this family, but it is suboptimal in the number of labels used to encode a tree. We derive an optimal hierarchical bracketing, which minimizes the number of symbols used and encodes projective trees using only 12 distinct labels (vs. 16 for the 4-bit encoding). We also extend optimal hierarchical bracketing to support arbitrary non-projectivity in a more compact way than previous encodings. Our new encodings yield competitive accuracy on a diverse set of treebanks.