Research on extracting military-target motion events from open-source texts
The extraction of event information and motion trajectory of designated military targets from massive unstructured open-source texts on military target motions constitutes a fundamental undertaking in identifying and forcasting the operational intentions of such targets and in mining battlefield dynamic information.Aiming at the problem that the current event extraction research ignores the spatial relationship information between place arguments,which leads to the inability to extract the trajectory of moving targets,we proposed a method of dividing fine-grained spatial relationship labels to identify spatial relationships,and extracting events through sequence labeling.A joint event extraction model that uses the pre-trained language model for underlying semantic encoding,bidirectional long short-term memory network for deep feature extraction,and conditional random fields for label classification to achieve motion event extraction.On the basis of the motion event extraction results,the motion trajectory extraction algorithm was used to enhance the spatial relationship information.Through experiments on the self-built real military target motion news dataset,the F1 score value of 84.0%was obtained.
open-source military intelligenceevent extractionspatial relation recognitiondeep learning
万方数据
维普
