  Local place names are frequently used by residents living in a geographic
region. Such place names may not be recorded in existing gazetteers, due to
their vernacular nature, relative insignificance to a gazetteer covering a
large area (e.g., the entire world), recent establishment (e.g., the name of a
newly-opened shopping center), or other reasons. While not always recorded,
local place names play important roles in many applications, from supporting
public participation in urban planning to locating victims in disaster
response. In this paper, we propose a computational framework for harvesting
local place names from geotagged housing advertisements. We make use of those
advertisements posted on local-oriented websites, such as Craigslist, where
local place names are often mentioned. The proposed framework consists of two
stages: natural language processing (NLP) and geospatial clustering. The NLP
stage examines the textual content of housing advertisements, and extracts
place name candidates. The geospatial stage focuses on the coordinates
associated with the extracted place name candidates, and performs multi-scale
geospatial clustering to filter out the non-place names. We evaluate our
framework by comparing its performance with those of six baselines. We also
compare our result with four existing gazetteers to demonstrate the
not-yet-recorded local place names discovered by our framework.

Information Retrieval