融合形态与句法特征，提升韩语依存句法分析准确率
Enhancing Korean Dependency Parsing with Morphosyntactic Features
- 统一依存与形态标注，保留句法依赖同时引入形态特征
- 在韩语数据集上，形态特征使依存分析准确率显著提升
- 适合研究多语言句法分析或韩语自然语言处理的开发者
本文提出UniDive for Korean，一个整合通用依存（UD）与通用形态学（UniMorph）的框架，以增强韩语的形态句法表示与处理。韩语丰富的屈折形态和灵活词序对现有框架构成挑战，因其常将形态与句法分离，导致语言分析不一致。UniDive通过保留句法依赖的同时融合UniMorph提取的形态特征，实现句法与形态标注的统一，提升标注一致性。我们构建了一个集成数据集并用于依存句法分析，实验表明，丰富形态句法特征显著提升了分析准确率，尤其在受形态影响的语法关系区分上表现更优。使用编码器单向与解码器单向模型的实验均证实，显式形态信息有助于更精确的句法分析。
原文摘要 · Abstract (English)
This paper introduces UniDive for Korean, an integrated framework that bridges Universal Dependencies (UD) and Universal Morphology (UniMorph) to enhance the representation and processing of Korean {morphosyntax}. Korean's rich inflectional morphology and flexible word order pose challenges for existing frameworks, which often treat morphology and syntax separately, leading to inconsistencies in linguistic analysis. UniDive unifies syntactic and morphological annotations by preserving syntactic dependencies while incorporating UniMorph-derived features, improving consistency in annotation. We construct an integrated dataset and apply it to dependency parsing, demonstrating that enriched morphosyntactic features enhance parsing accuracy, particularly in distinguishing grammatical relations influenced by morphology. Our experiments, conducted with both encoder-only and decoder-only models, confirm that explicit morphological information contributes to more accurate syntactic analysis.
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