Chinese Text Readability Grading via Multi-level Linguistic Feature Fusion
The goal of Chinese text readability grading task is to classify Chinese texts into the appropriate difficulty levels for readers.Recent studies have shown that linguistic features and deep semantic features are complementary in characterizing the difficulty of text.However,existing work only performed shallow fusion of these two types of features,and deep,multi-level fusion has not been considered.Therefore,this paper develops a multi-level lin-guistic feature fusion strategy based on the traditional text readability grading model on BERT.Specifically,consid-ering the interaction of different linguistic features and network layer structures,this paper fused the linguistic fea-tures of characters,words and grammar in the embedding layer as well as the self-attention layer.The experimental results show that the proposed method outperforms all baseline models and by 94.2％accuracy.
Chinese text readability gradingmulti-level linguistic feature fusiondeep model
万方数据
维普
