  A vast amount of location information exists in unstructured texts, such as
social media posts, news stories, scientific articles, web pages, travel blogs,
and historical archives. Geoparsing refers to the process of recognizing
location references from texts and identifying their geospatial
representations. While geoparsing can benefit many domains, a summary of the
specific applications is still missing. Further, there lacks a comprehensive
review and comparison of existing approaches for location reference
recognition, which is the first and a core step of geoparsing. To fill these
research gaps, this review first summarizes seven typical application domains
of geoparsing: geographic information retrieval, disaster management, disease
surveillance, traffic management, spatial humanities, tourism management, and
crime management. We then review existing approaches for location reference
recognition by categorizing these approaches into four groups based on their
underlying functional principle: rule-based, gazetteer matching-based,
statistical learning-based, and hybrid approaches. Next, we thoroughly evaluate
the correctness and computational efficiency of the 27 most widely used
approaches for location reference recognition based on 26 public datasets with
different types of texts (e.g., social media posts and news stories) containing
39,736 location references across the world. Results from this thorough
evaluation can help inform future methodological developments for location
reference recognition, and can help guide the selection of proper approaches
based on application needs.
