Extracting Relationship Among Characters from Local Chronicles with Text Structures and Contents
Wang Yongsheng,Wang Hao(),Yu Wei,Zhou Zeyu
School of Information Management, Nanjing University, Nanjing 210023, China Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This study proposes a new method to extract relationship among characters from local chronicles, aiming to explore the culture and history information embedded in Yiwu Local Chronicles—Chapter of Persons. [Methods] We constructed the relationship extraction model based on text structures and contents. For text structures, we used the rule templates and word features to extract relationship from the original texts, which was also categorized with different granularity. For the text contents, we introduced a remotely supervised approach to extract relationship. Then, we combined the BERT+Bi-GRU+ATT and BERT+FC deep learning models to transform the relationship extraction to a multi-label classification task. Finally, we reduced the impacts of the noise from remote supervision on the model’s accuracy by correcting relationship labels. [Results] The proposed method realized high automation and yielded better extracted information. The BERT+FC models improved the F1 values by up-to 27%, while different relationship categories showed some affinity. The F1 value of the “strong co-occurrence relationship” was increased by 3% after label correction. [Limitations] We only investigated the relationships among characters in local chronicles. [Conclusions] The new method could effectively extract relationship among the same type of entities in historical Chinese documents.
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Wang Yongsheng, Wang Hao, Yu Wei, Zhou Zeyu. Extracting Relationship Among Characters from Local Chronicles with Text Structures and Contents. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 318-328.
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