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.

通过自动提取文本信息来感知运动现象具有挑战性。本文介绍了 GeoMovement，该系统基于结合机器学习和基于规则的运动相关信息抽取，并使用先进的可视化技术。GeoMovement 提供了一种集成框架，可以同时提取运动和缺乏运动，为人们提供了有意义的地理运动信息。