A Fine-Grained Geolocalization Method for User Generated Short Text

Recently, the fine-grained geolocalization of User-Generated Short Text (UGST) has been increasingly attracting much attention. Accurate geolocation can benefit many applications, especially for the location-based services. However, since the majority of UGSTs are short, noisy and not geotagged, existing methods still suffer from two issues. One is the heavy reliance on the manual features not fully exploiting the semantic information. Another is the free writing style of social media resulting in extremely few useful geo-indicative information. To address these issues, we propose a novel Fine-grained Geolocalization method for UGSTs with Preprocessing, location-entity consistency Replacing, Filtering, Convolutional neural network (FG-PRFC), which only relies on UGST itself. Compared to existing methods, FG-PRFC has four unique characteristics: (1) We present a UGST-oriented preprocessing method to obtain more semantic information. (2) To tackle the abbreviation issue, we develop a replacing method to allow geo-indicative words behaving in the same way. (3) Following the idea of TFIDF, we weight the words in UGST and then develop a location-free UGST filtering method. (4) We employ convolutional neural network to model the relationship between words and locations. Extensive experiments on three ground-truth datasets demonstrate that our method has a significant improvement compared to state-of-art methods. (c) 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.