A RoBERTa-Based Functional Syntax Annotation Model for Chinese Texts
This provides a new automated method for Chinese functional syntax analysis, addressing a gap in applying Systemic Functional Grammar to Chinese, though it is incremental as it adapts existing NLP techniques to a specific domain.
The paper tackled the lack of an automatic functional syntax annotation system for Chinese texts by developing a RoBERTa-based model, achieving an F1 score of 0.852 on a test set, which significantly outperformed other models in identifying core syntactic elements.
Systemic Functional Grammar and its branch, Cardiff Grammar, have been widely applied to discourse analysis, semantic function research, and other tasks across various languages and texts. However, an automatic annotation system based on this theory for Chinese texts has not yet been developed, which significantly constrains the application and promotion of relevant theories. To fill this gap, this research introduces a functional syntax annotation model for Chinese based on RoBERTa (Robustly Optimized BERT Pretraining Approach). The study randomly selected 4,100 sentences from the People's Daily 2014 corpus and annotated them according to functional syntax theory to establish a dataset for training. The study then fine-tuned the RoBERTa-Chinese wwm-ext model based on the dataset to implement the named entity recognition task, achieving an F1 score of 0.852 on the test set that significantly outperforms other comparative models. The model demonstrated excellent performance in identifying core syntactic elements such as Subject (S), Main Verb (M), and Complement (C). Nevertheless, there remains room for improvement in recognizing entities with imbalanced label samples. As the first integration of functional syntax with attention-based NLP models, this research provides a new method for automated Chinese functional syntax analysis and lays a solid foundation for subsequent studies.