关键词: 自然语言处理, 图卷积网络, 中文语义错误识别, 大语言模型, 依存句法分析

Abstract: Chinese semantic errors are different from simple spelling mistakes and grammatical errors, which are hidden in a deeper semantic level. Chinese semantic error recognition aims to determine whether a Chinese sentence contains semantic errors or not, and the performance of the recognition model is crucial for semantic error proofreading as a pre-task of semantic error correction. For Chinese semantic error recognition, Hierarchical Information Enhancement Graph Convolutional Network (HIE-GCN) is proposed in the paper. It aims to reduce the difference between the tree structure and the context structure by embedding the hierarchical information of the nodes in the syntactic tree. First, a traversal algorithm is used to extract the hierarchical information of the nodes in the syntactic tree; second, the hierarchical information embedding is added to the BERT model to generate character features, and the graph convolutional neural network uses the character features for the nodes on the graph, and the feature vector of the whole sentence is obtained after the graph convolutional computation; lastly, the fully-connected layer is used to perform the single-category or multi-category semantic error recognition. Experiments on the FCGEC dataset show that HIE-GCN improves recognition accuracy and F1 scores by 0.6% to 3.7% over baseline methods. Ablation studies confirm the value of hierarchical information embedding, and the model significantly outperforms large language models in recognition tasks. In error correction, using a pipeline improves accuracy by 8.01%. Additionally, prompting the LLM with the type of sentence error improves error correction accuracy by 4.62%.

Key words: Natural Language Processing, Graph Convolutional Network, Chinese Semantic Error Recognition, Large Language Models, Dependency Parsing