Research on the construction of event recognition model in historical books based on text generation technology

Subjects: Library Science，Information Science >> Automation method and equipment in intelligence process submitted time 2022-08-31

Wang, Yanying Wang, Hao Zhu, Hui Li, Xiaomin

Abstract： Objective In order to construct a event recognition model in historical books, the performance of sequence labeling method in event recognition in historical ancient books is compared with that of text generation method. Methods In this paper, "Three Kingdoms" is selected as the original corpus. To compare the performance of the two methods, performing on the "Three Kingdoms" event data set, the sequence labeling experiment used BMES annotation and builded the BBCN-SG model ,and the text generation experiment builded the T5-SG model.It also builded RoBERTa-SG and NEZHA-SG models to conduct comparative experiments on generative models. Combining three text generation models and integrating the idea of Stacking ensemble learning, the Stacking-TRN-SG model is constructed. Results On the subject of modeling event recognition in historical ancient books, the performance of the text generation method is significantly better than that of the sequence labeling method. In the text generation method, the performance of the three models is RoBERTa-SG > T5-SG > NEZHA-SG. Stacking ensemble learning greatly improves the recognition performance of generation models. Limitations The computational resources of this paper are limited, and the Stacking-TRN-SG model lacks application research in other historical and ancient corpora. Conclusions The Stacking-TRN-SG model constructed in this paper preliminarily realizes the automatic event recognition of historical ancient books.

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