In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there’s a need for Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of the data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive Text Sequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in the data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility, the results show that this method can transfer task knowledge between multiple different domains in the data-poor scenarios and achieve SOTA performance.

近年来，对大规模人员流动的知识探索受到了广泛关注。为了实现对人类行为的语义理解并揭示大规模人类移动的模式，命名实体识别（NER）是一项关键技术。物联网和 CPS 技术的快速进步导致人们从各种来源收集大量人员流动数据。因此，需要跨域NER，它可以从源域传输实体信息，以自动识别和分类不同目标域文本中的实体。在数据匮乏的情况下，如何跨时间和空间传递人类移动知识就显得尤为重要，因此本文提出了自适应文本序列增强模块（at-SAM）来帮助模型增强句子中实体之间的关联在数据匮乏的目标领域。本文还提出了预测标签引导的双序列感知信息模块（Dual-SAM）来提高标签信息的可转移性。在包含人类移动隐藏知识的领域进行了实验，结果表明该方法可以在数据贫乏的场景下在多个不同领域之间转移任务知识并实现 SOTA 性能。