文本流中路线图在线抽取模型OA

This paper transformed user spatial transfer information contained in text streams into route maps to provide users with intuitive route and experience visualization.Firstly,it proposed a large model-based spatiotemporal event extraction model.The model constructed a route knowledge framework and extraction templates.It used a fine-tuning dataset for model adapta-tion.Then,the paper proposed a large model-based text segmentation model.This model employed text partitioning templates and a fine-tuning dataset for adaptation.Next,the paper proposed an online path generation method.This method designed in-ference techniques for entity attributes and relationships.It adopted a weighted scoring strategy to generate optimal paths.Final-ly,the experiments performed on real-world datasets.Results show that the proposed models achieve better performance than other large models in text segmentation and event extraction tasks.The proposed method increases entity reasoning accuracy by 16％compared to multi-feature entity matching methods.These findings validate the effectiveness of the proposed models and method.