亨茨维尔、医院和冰球队:名字可以透露你的位置

NUT@EMNLP Pub Date : 2017-09-01 DOI:10.18653/v1/W17-4415
Bahar Salehi, Dirk Hovy, E. Hovy, Anders Søgaard
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引用次数: 8

摘要

地理定位是识别社交媒体用户的主要位置的任务,在自然语言处理中,关于社交媒体帖子的自动分析能在多大程度上提供帮助的文献越来越多。然而,并不是所有的内容功能都能同样暴露用户的位置。在本文中,我们评估了九种名称实体(NE)类型。使用各种指标,我们发现GEO-LOC、FACILITY和SPORT-TEAM比其他网元类型更能提供地理定位信息。使用这些类型,我们提高了地理定位精度,并减少了各种著名的基于文本的方法的距离误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Huntsville, hospitals, and hockey teams: Names can reveal your location
Geolocation is the task of identifying a social media user’s primary location, and in natural language processing, there is a growing literature on to what extent automated analysis of social media posts can help. However, not all content features are equally revealing of a user’s location. In this paper, we evaluate nine name entity (NE) types. Using various metrics, we find that GEO-LOC, FACILITY and SPORT-TEAM are more informative for geolocation than other NE types. Using these types, we improve geolocation accuracy and reduce distance error over various famous text-based methods.
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