论文标题
通过地理分析探索运动的描述
Exploring Descriptions of Movement Through Geovisual Analytics
论文作者
论文摘要
使用自动从文本中提取信息的感官是一个具有挑战性的问题。在本文中，我们介绍了一种特定类型的信息提取类型，即提取与运动描述有关的信息。汇总和理解与移动描述以及文本中缺乏运动有关的信息可以提高对各种类型的运动现象的理解和感知，例如，人和动物的迁移，由于Covid-19的行驶而驱动的障碍，由于COVID-19的旅行等。我们呈现一种基于机器学习和基于规则的运动的系统，该系统是基于机器学习和基于状态的领域的系统，并呈现了领域的影响。除了对运动的描述外，我们的工具还可以提取并表现出缺乏运动。关于自动提取运动的描述，尤其是否定和运动，几乎没有事先工作。除了解决这些问题之外，GeoMovement还提供了一个新颖的集成框架，用于将这些提取模块与可视化结合在一起。我们包括两个系统的案例研究，这些案例研究表明人类如何获得有意义的地理运动信息。地理位置可以补充精确的移动数据，例如，使用传感器获得，或者在无法使用精确数据时自行使用。
Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable.
