The level of intelligence in administrative law enforcement is a manifestation of the modernization of national governance capacity,and data is an important support for the development of intelligence.In the field of administrative law enforcement,various administrative organs store numerous historical cases recorded in textual form.These cases are unstructured data with low value density and limited usability.The use of event extraction technology for the quick and efficient extraction of structured information,such as the type of case authority and the time and place of case occurrence,from administrative law enforcement case texts can promote the utilization of historical case records and provide support for the study of intelligent law enforcement.This study collects and organizes real case data for a city and constructs a dataset in the field of administrative law enforcement through manual annotation.Considering text characteristics,such as no trigger words,document-level text,and unfixed format,the study then proposes a two-stage event extraction method based on a Bidirectional Encoder Representations from Transformers(BERT)model and a Bi-directional Long Short-Term Memory network with Conditional Random Field(BiLSTM-CRF)model,which sequentially detects event types and identifies event arguments through text multi-classification and sequence annotation.Experimental results show that the F1 values of event-type detection and event-argument extraction tasks reach 99.54％and 97.36％,respectively,thus realizing the effective extraction of case information.
administrative law enforcement case/event extraction/two-stage method/Bidirectional Encoder Representations from Transformers(BERT)model/Bi-directional Long Short-Term Memory network with Conditional Random Field(BiLSTM-CRF)model
维普
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