Extracting fine-grained location with temporal awareness in tweets: A two-stage approach

Twitter has attracted billions of users for life logging and sharing activities and opinions. In their tweets, users often reveal their location information and short term visiting histories or plans. Capturing user’s short term activities could benefit many applications for providing the right context at the right time and location. In this paper, we are interested in extracting locations mentioned in tweets at fine-grained granularity, with temporal awareness. More specifically, we like to recognise the point-of-interests (POI) mentioned in a tweet and predict whether the user has visited, is currently at, or will soon visit the mentioned POIs. A POI can be a restaurant, a shopping mall, a bookstore or any other fine-grained location. Our proposed framework, named TS-Petar (Two-Stage POI Extractor with Temporal Awareness), consists of two main components: a POI inventory and a two-stage time-aware POI tagger. The POI inventory is built by exploiting the crowd wisdom of Foursquare community. It contains both POIs’ formal names and their informal abbreviations, commonly observed in Foursquare check-ins. The time-aware POI tagger, based on the Conditional Random Field (CRF) model, is devised to disambiguate the POI mentions and to resolve their associated temporal awareness accordingly. Three sets of contextual features (linguistic, temporal, and inventory features) and two labeling schema features (OP and BILOU schemas) are explored for the timeaware POI extraction task. Our empirical study shows that the subtask of POI disambiguation and the subtask of temporal awareness resolution call for different feature settings for best performance. We have also evaluated the proposed TS-Petar against several strong baseline methods. The experimental results demonstrate that the twostage approach achieves the best accuracy and outperforms all baseline methods in terms of both effectiveness and