Automatic Identification of Locative Expressions from Social Media Text: A Comparative Analysis

With the proliferation of smartphones and the increasing popularity of social media, people have developed habits of posting not only their thoughts and opinions, but also content concerning their whereabouts. On such highly-interactive yet informal social media platforms, people make heavy use of informal language, including when it comes to locative expressions. Such usage inhibits the ability of traditional Natural Language Processing approaches to retrieve geospatial information from social media text. In this research, we: (1) develop a medium-scale corpus of "locative expressions" derived from a variety of social media sources; (2) benchmark the performance of a range of geoparsers over the corpus, with the finding that even the best-performing systems are substantially lacking; and (3) carry out extensive error analysis to suggest ways of improving the accuracy and robustness of geoparsers.