[Objective] This paper constructs a framework to extract events from ancient books, which uses the RoBERTa-CRF model to identify event types, argument roles and arguments. [Methods] We collected the war sentences from Zuozhuan as the experimental data, which helped us establish the classification schema for event types and argument roles. Based on the RoBERTa-CRF model, we used the multi-layer transformer to extract the corpus features, which were combined with the sequence tags to learn the correlation constraints. Finally, we identified and extracted the arguments by the tag sequence. [Results] The accuracy, recall and F1 values of the proposed model were 87.6%, 77.2% and 82.1%, which were higher than results of the GuwenBERT-LSTM, Bert-LSTM, RoBERTa-LSTM, Bert-CRF and RoBERTa-CRF on the same dataset. [Limitations] The size of the experimental dataset needs to be expanded, which could make the topic categories more balanced. [Conclusions] The RoBERTa-CRF model constructed in this paper could effectively extract events from ancient Chinese books.
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Yu Xuehan, He Lin, Xu Jian. Extracting Events from Ancient Books Based on RoBERTa-CRF. Data Analysis and Knowledge Discovery, 2021, 5(7): 26-35.
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