Event Extraction for Military Target Motion in Open-source Military News

Yuheng Zhang, Sheng Zheng, Zhang Sheng
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Abstract

Open-source military news is the data source of open-source intelligence analysis, which can assist intelligence analysis by event extracting. Military events include the military target motion events which involve changes in multiple places. It is very important to infer the movement trajectory of a military target from the location-changing process of multiple place arguments. However, the traditional event extraction cannot reflect the location-changing process of multiple place arguments during the target moving. To solve this problem, this paper designs multiple place roles to learn location semantics relationship among place arguments for reflecting the location-changing process of multiple place arguments. We adopt the method of sequence labeling to convert military target moving event extraction into a multi-classification problem. This paper uses the pre-trained model Roberta for feature learning and a linear layer for a trigger word (or argument) classification. Since there is no standard military event dataset, this paper constructs an event dataset of military target moving. On this dataset, the F1 of event extraction reaches 72.7%, which is a certain improvement compared with each benchmark model.
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开源军事新闻中军事目标运动的事件提取
开源军事新闻是开源情报分析的数据源,可以通过事件提取来辅助情报分析。军事运动项目包括涉及多地变化的军事目标运动项目。从多个地点参数的位置变化过程中推断军事目标的运动轨迹是非常重要的。然而,传统的事件提取方法无法反映目标移动过程中多个地点参数的位置变化过程。为了解决这一问题,本文设计了多个地点角色来学习地点参数之间的位置语义关系,以反映多个地点参数的位置变化过程。采用序列标注的方法,将军事目标运动事件提取转化为多分类问题。本文使用预训练模型Roberta进行特征学习,并使用线性层进行触发词(或参数)分类。由于没有标准的军事事件数据集,本文构建了一个军事目标移动事件数据集。在该数据集上,事件提取的F1达到72.7%,与各基准模型相比有一定的提高。
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