Recognition and application of textbook moves facilitated by large language model
To solve the problems existing in printed textbooks,such as the inability to quickly locate knowledge concepts,the difficulty to grasp the logical structure of textbook writing literally,and the difficulty to establish the correlation between knowledge,textbook moves recognition method facilitated by large language model was proposed.Firstly,textbook move structure was designed and a dataset for textbook move classification was constructed.Then,a generative large language model was used to generate corpus and enhance features for scarce and indistinct steps,respectively.Finally,by combining the move recognition dataset and enhanced move data,the initial model of textbook move recognition was fine-tuned to obtain a textbook move recognition model that combines the large language model.The experimental results show that compared with the initial model BERT-wwm-ext,the overall accuracy of the move recognition model facilitated by the large language model has increased by 5.06 percentage points,reaching 95.44％,and the Macro-F1 value has increased by 2.54 percentage points,reaching 93.51％.Furthermore,the move recognition model was utilized to construct a knowledge graph and an after-book-index,effectively elucidating the logical structure of textbook with heightened clarity.
digital textbookmove recognitionlarge language modelknowledge graphafter-book-index
万方数据
维普
