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引用次数: 0

摘要

具有GPS功能的移动设备的普遍可用性和社交媒体平台的普及为具有时空信息的文本数据创造了丰富的来源。此外,其他领域,如犯罪事件描述和搜索引擎查询,可以提供时空文本数据。这些数据源可用于发现人类行为的时空相关见解。这项工作的重点是与特定时间和地点相关的文本建模。我们将传统的语言建模任务从自然语言处理扩展到时空条件下的语言建模。这个任务定义允许我们使用语言建模中使用的相同的评估框架。用于时空文本数据表示的模型应该能够捕获指导文本如何在时空上下文中生成的模式。我们的目标是开发基于时空变量的语言建模神经网络模型,并能够捕获诸如:邻域、周期性和层次结构等属性。
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Spatio-temporal Conditioned Language Models
The ubiquitous availability of mobile devices with GPS capabilities and the popularity of social media platforms have created a rich source for textual data with spatio-temporal information. Also, other domains like crime incident description and search engine queries, can provide spatio-temporal textual data. These data sources can be used to discover space-time related insights of human behavior. This work focuses on modeling text that is associated with a particular time and place. We extend the traditional language modeling task from natural language processing to language modeling under spatio-temporal conditions. This task definition allows us to use the same evaluation framework used in language modeling. A model for spatio-temporal text data representation should be able to capture the patterns that guide how text is generated in a spatio-temporal context. We aim to develop neural network models for language modeling conditioned on spatio-temporal variables with the ability to capture properties such as: neighborhood, periodicity and hierarchy.
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