Fine-Grained Geolocalization of User-Generated Short Text based on Weight Probability Model
Congjie Gao, Yongjnu Li, Jiaqi Yang · 2019
Recently, the fine-grained geolocalization of User-Generated Short Text (UGST) has become increasingly important. Existing methods can not make full use of the location information in the UGSTs. Besides, existing works only consider the importance of terms for all locations, but do not distinguish the importance of the same term in different locations. To solve these problems, we propose a fine-grained geolocalization method based on a weight probability model (FGST-WP). The method mainly includes three parts: 1) Using the reverse maximum match algorithm to filter out UGSTs that do not contain any location indicative information. 2) Building coupling of terms and locations and adopting a mixed weight strategy to assign weights to terms. 3) Calculating the probability of non-geotagged UGST posted from each location and selecting k locations according to the top-k probabilities. Experiments on ground-truth datasets prove the superior performance of FGST-WP.