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

• URL: http://arxiv.org/abs/2609.06070v1
• Date: Sat, 05 Sep 2026 12:58:58 GMT
• Title: DPH Parser: A Bottom-Up Grammar-Driven Parser for Joint Constituency and Dependency Analysis
• Abstract summary: This paper presents a grammar-driven bottom-up unsupervized parsing framework inspired by Generalized Phrase Structure Grammar (GPSG) and Head-driven Phrase Structure Grammar (HPSG)<n>The system combines probabilistic POScorpora tagging, phrase projection, and weighted hypotheses to process realistic and partially noisy text input.<n>Although the current system does not yet approach the accuracy of modern neurals, the results demonstrate the feasibility of applying transparent rule-based bottom-up parsing to treebank data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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