基于规则的语法解析器，能同时生成句法成分和依存结构。
DPH Parser: A Bottom-Up Grammar-Driven Parser for Joint Constituency and Dependency Analysis
- 自底向上构建句法结构，用少量特征化规则逐步组合短语。
- 在真实文本上实现53.32%和52.58%的无标签依存得分。
- 结果可解释性强，适合需要透明性的自然语言处理任务。
本文提出依赖-短语层级解析器（DPH Parser），一种受广义短语结构语法（GPSG）和主导短语结构语法（HPSG）启发的规则驱动、自底向上的无监督解析框架。该系统通过紧凑的特征化句法规则集逐步构建成分结构，并借助显式头标注推导依存关系。结合概率词性标注、递归短语投影和加权解析假设，可处理真实且部分噪声的文本输入。与纯神经数据驱动解析器不同，其句法推导过程具有明确可解释性。在通用依存库（UD）英文语料上评估，以无标签依存得分（UAS）为主要指标，对比Stanza和spaCy解析器。在小规则量下，DPH解析器在开发集和测试集分别达到53.32%和52.58%的UAS；相同数据下，Stanza分别为89.12%和88.67%，spaCy为56.91%和58.59%。尽管当前性能尚未达到现代神经解析器水平，但结果表明透明规则驱动的自底向上解析在真实语料上联合生成成分与依存结构具有可行性。
原文摘要 · Abstract (English)
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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