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Extraction of legal elements is an important basis for judicial intelligent auxiliary applications, and its purpose is to identify the key elements involved in the judgment document.In the past, extracting legal elements usually used multi-label classification methods for modeling.These methods mainly relied on the text features of the judgment document, thereby ignoring the label features.Besides, due to the imbalanced data problem in judicial data sets, the classification method will lead to poor model performance because of too many negative examples.To solve the above problems, a legal element extraction method based on BERT reading comprehension framework was proposed.This method constructed auxiliary questions with label information and legal prior knowledge, and used the machine reading comprehension model based on BERT to establish the semantic associations between question and judgment document.And this method added special tokens before and after the label in the question to enhance the learning ability of the model.Experiments were conducted on the legal element extraction data sets of the CAIL2019.Experiment results show that the performance is improved significantly, and the F1 value has been increased by 2.7%, 11.3%, and 5.6% respectively on the data sets of marriage and family case, labor dispute case, and loan contract dispute case. |
