用经典语法理论解决解析难题，兼顾准确与可解释性。
We can still parse using syntactic rules
- 结合上下文无关语法与广义短语结构语法，设计新解析算法
- 在19个语料上平均无标签依存得分达53.8%，支持多假设生成
- 适合需要透明推理的自然语言处理场景
本研究提出一种基于早期上下文无关语法（CFG）和广义短语结构语法（GPSG）的新解析方法，包含新算法与语法规则，克服了传统CFG的局限。该系统可同时生成依存与成分句法树，并能处理噪声和不完整解析。在通用依存数据集上的测试显示，在7个语料的开发集上平均无标签依存得分（UAS）为54.5%，在12个语料的测试集上为53.8%。系统还可生成多个解析假设，支持后续重排序以提升准确率。该方法整合了自1950年代以来大量理论语法成果，实现可解释的计算语言处理。
原文摘要 · Abstract (English)
This research introduces a new parsing approach, based on earlier syntactic work on context free grammar (CFG) and generalized phrase structure grammar (GPSG). The approach comprises both a new parsing algorithm and a set of syntactic rules and features that overcome the limitations of CFG. It also generates both dependency and constituency parse trees, while accommodating noise and incomplete parses. The system was tested on data from Universal Dependencies, showing a promising average Unlabeled Attachment Score (UAS) of 54.5% in the development dataset (7 corpora) and 53.8% in the test set (12 corpora). The system also provides multiple parse hypotheses, allowing further reranking to improve parsing accuracy. This approach also leverages much of the theoretical syntactic work since the 1950s to be used within a computational context. The application of this approach provides a transparent and interpretable NLP model to process language input.
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