# DPH Parser: A Bottom-Up Grammar-Driven Parser for Joint Constituency and Dependency Analysis

arXiv: https://arxiv.org/abs/2609.06070 · HTML: https://mlsanity.com/paper/2609.06070/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Head-Driven Phrase Structure Grammar Parsing on Penn Treebank](https://mlsanity.com/paper/1907.02684/) (2019)
- [Revisiting Supertagging for Faster HPSG Pasing](https://mlsanity.com/paper/2309.07590/) (2023)
- [Head-driven Phrase Structure Parsing in O(n3) Time Complexity](https://mlsanity.com/paper/2105.09835/) (2021)
- [An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese](https://mlsanity.com/paper/2411.17270/) (2024)

## Abstract (arXiv)

This paper presents Dependency-Phrase Hierarchy Parser (DPH Parser), a grammar-driven bottom-up unsupervized parsing framework inspired by Generalized Phrase Structure Grammar (GPSG) and Head-driven Phrase Structure Grammar (HPSG). The parser incrementally constructs constituency structures using a compact inventory of feature-based syntactic rules while deriving dependency relations through explicit head annotations. The system combines probabilistic POS tagging, recursive phrase projection, and weighted parse hypotheses to process realistic and partially noisy text input. Unlike purely neural and data-driven parsers, the resulting syntactic derivations remain explicitly interpretable. We evaluated parser performance on English corpora from the Universal Dependencies (UD) project using Unlabeled Attachment Score (UAS) as the main parsing metric, comparing the outcomes against Stanza and spaCy parsers. For a small inventory of syntactic rules, DPH parser achieved UAS values of 53.32% & 52.58% (UD Devset/Testset respectively). For the same data, Stanza achieved 89.12% & 88.67% while spaCy achieved 56.91% and 58.59%. Although the current system does not yet approach the accuracy of modern neural parsers, the results demonstrate the feasibility of applying transparent rule-based bottom-up parsing to realistic treebank data while jointly producing constituency and dependency structures.