文本中隐含地理运动的识别
Recognition of Implicit Geographic Movement in Text
摘要：
分析人类、动物和其他现象的地理运动是一个不断发展的研究领域。这项研究有利于城市规划，物流，动物迁徙的理解，等等。通常，移动被捕获为精确的地理坐标和全球定位系统（GPS）的时间戳。虽然一些研究使用计算技术来利用隐含的运动来描述路线方向，徒步旅行路径和历史探索路线，但创新将随着大型和多样化的语料库而加速。我们创建了一个句子语料库，标记为描述地理运动或不包括实体移动的类型。事实证明，在没有任何可比语料库的情况下创建这个语料库是很困难的，人类标记成本很高，而且运动有时会被不同地解释。为了克服这些挑战，我们开发了一个迭代过程，采用手工标记，人群投票确认，和机器学习来预测更多的标签。通过将词嵌入的进步与传统的机器学习模型和模型集成相结合，预测精度处于可接受的水平，以产生大型银标准语料库，尽管金标准语料库训练集很小。我们的语料库将可能有利于计算处理的地理文本和空间认知，除了检测运动。
英文摘要：
Analyzing the geographic movement of humans, animals, and other phenomena is a growing field of research. This research has benefited urban planning, logistics, animal migration understanding, and much more. Typically, the movement is captured as precise geographic coordinates and time stamps with Global Positioning Systems (GPS). Although some research uses computational techniques to take advantage of implicit movement in descriptions of route directions, hiking paths, and historical exploration routes, innovation would accelerate with a large and diverse corpus. We created a corpus of sentences labeled as describing geographic movement or not and including the type of entity moving. Creating this corpus proved difficult without any comparable corpora to start with, high human labeling costs, and since movement can at times be interpreted differently. To overcome these challenges, we developed an iterative process employing hand labeling, crowd voting for confirmation, and machine learning to predict more labels. By merging advances in word embeddings with traditional machine learning models and model ensembling, prediction accuracy is at an acceptable level to produce a large silver-standard corpus despite the small gold-standard corpus training set. Our corpus will likely benefit computational processing of geography in text and spatial cognition, in addition to detection of movement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
