An Entity-Based Fine-Grained Geolocalization of User Generated Short Text

Recently, the fine-grained geolocalization of user-generated short text (UGST), which can benefit many location-based applications, has been attracting the attention of academica. The semantic information in UGST is seldom introduced in most existing work, which reduces the effectiveness of existing methods. To address this issue, we propose an entity-based fine-grained geolocalization of UGST, which consists of following steps. (1) We employ location-based social network to model the coupling between entities and locations, which can introduce much semantic information. (2) We extract entities from non-geotagged UGST, and discards this UGST if it has not location-related entities. Otherwise, (3) we utilize the built coupling model to rank the candidate locations for this UGST, and then select top $n$ locations as the result. The experiments demonstrate that our method shows marked improvement on $Accuracy\text{@}1km$ and average error distance compared to the state-of-the-art FRV, WMV and LW methods.