Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability.To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework. Partial syntactic rules from a source domain are combined with target domain sentences to dynamically generate training data, enhancing the adaptability of the parser to diverse domains.Experiments conducted on textbook and news domains demonstrate the effectiveness of the proposed method, outperforming rule-based baselines by 1.68 points on F1 metrics.

翻译：句式结构解析是一种主要应用于语言教学的句法分析方法。现有句式结构解析器严重依赖教材语料库进行训练，缺乏跨领域能力。为突破这一限制，本文提出了一种创新方法，在自训练框架中利用大语言模型（LLMs）。该方法将源领域的部分句法规则与目标领域句子相结合，动态生成训练数据，从而增强解析器对不同领域的适应性。在教材和新闻领域进行的实验表明，所提方法具有有效性，在F1指标上比基于规则的基线方法高出1.68个点。