用语法结构预测叙事文风，准确率达94.8%
Syntactic Patterns and Stylistic Functions in Narrative Prose: A Rule-Based and Machine-Learning Approach
- 基于句法三元组构建句子语法特征表示
- 机器学习模型在五类文风上达0.948宏F1
- 开源流程可复现，适合文本风格研究
本文开展了一项小规模量化实验，将句法结构与叙事文体功能关联。基于3,300句的依存句法标注语料，通过透明规则系统对句子进行五类文体标注：描述性、内省性、因果性、意识形态性和中性。每个句子被转化为由词干、词性标签和依存关系组成的线性三元组序列，作为机器学习分类器的输入。最佳模型在十折交叉验证下达到0.948的宏平均F1分数。实验全程使用Python与开源工具实现。本研究不旨在建立完整文体理论，而是提供一种可复现、可扩展的工作流，用于探索语法结构如何影响叙事理解。
原文摘要 · Abstract (English)
This paper presents a small-scale quantitative experiment that links syntactic structure to stylistic functions in narrative prose. Starting from a dependency-parsed corpus of 3,300 sentences, we derive sentence-level stylistic labels across five categories --- descriptive, introspective, causal, ideological, and neutral --- using a transparent rule-based procedure that inspects lemmas, universal part-of-speech tags, and syntactic relations. For each sentence we construct a compact representation of its syntactic profile as a sequence of linearised triples combining lemma, POS tag, and dependency relation. These patterns serve as input to standard machine-learning classifiers trained to predict sentence-level style. The best-performing model achieves a macro-F1 of 0.948 under 10-fold cross-validation. The experiment is implemented entirely in Python using open-source tools. Our goal is not to propose a fully fledged stylistic theory, but to offer a reproducible and extensible workflow for exploring how grammatical structure contributes to narrative interpretation.
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