使用基于神经网络的双语地名识别模型从运动和锻炼相关的社交媒体信息中提取位置

IF 1.8 Q2 GEOGRAPHY Journal of Spatial Information Science Pub Date : 2022-06-20 DOI:10.5311/josis.2022.24.167
Pengyuan Liu, Sonja Koivisto, Tuomo Hiippala, Charlotte Van der Lijn, Tuomas Vaisanen, Marisofia Nurmi, T. Toivonen, Kirsi Vehkakoski, Janne Pyykonen, Ilkka Virmasalo, Mikko Simula, Elina Hasanen, Anna-Katriina Salmikangas, P. Muukkonen
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引用次数: 3

摘要

体育和锻炼有助于城市居民的健康和福祉。虽然以前的研究主要集中在特定地点的活动,如体育设施,但在城市任意地点发生的“非正式体育”在很大程度上被忽视了。观察这些活动更具挑战性,但这一挑战可以通过使用从社交媒体平台收集的数据来解决,因为社交媒体用户经常在特定地点生成与体育和锻炼相关的内容。这样就可以研究所有的运动,包括那些在任意地点进行的“非正式运动”,从而更好地了解城市中的体育和与运动相关的活动。然而,社交媒体平台上可用的用户生成的地理信息正变得越来越稀缺和粗糙。这使得从社交媒体上的自由文本内容中提取位置信息变得更加重要,而多语言和非正式语言使这一问题变得复杂。为了支持这一努力,本文提出了一种基于端到端深度学习的双语地名识别模型,用于从与体育和锻炼相关的社交媒体内容中提取位置信息。我们表明,我们的方法优于五种最先进的深度学习和机器学习模型。我们进一步展示了如何将我们的模型部署在地质测量框架中,以支持城市规划者促进健康和积极的生活方式。
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Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model
Sport and exercise contribute to health and well-being in cities. While previous research has mainly focused on activities at specific locations such as sport facilities, "informal sport" that occur at arbitrary locations across the city have been largely neglected. Such activities are more challenging to observe, but this challenge may be addressed using data collected from social media platforms, because social media users regularly generate content related to sports and exercise at given locations. This allows studying all sport, including those "informal sport" which are at arbitrary locations, to better understand sports and exercise-related activities in cities. However, user-generated geographical information available on social media platforms is becoming scarcer and coarser. This places increased emphasis on extracting location information from free-form text content on social media, which is complicated by multilingualism and informal language. To support this effort, this article presents an end-to-end deep learning-based bilingual toponym recognition model for extracting location information from social media content related to sports and exercise. We show that our approach outperforms five state-of-the-art deep learning and machine learning models. We further demonstrate how our model can be deployed in a geoparsing framework to support city planners in promoting healthy and active lifestyles.
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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
5
审稿时长
9 weeks
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