  Extracting location names from informal and unstructured social media data
requires the identification of referent boundaries and partitioning compound
names. Variability, particularly systematic variability in location names
(Carroll, 1983), challenges the identification task. Some of this variability
can be anticipated as operations within a statistical language model, in this
case drawn from gazetteers such as OpenStreetMap (OSM), Geonames, and DBpedia.
This permits evaluation of an observed n-gram in Twitter targeted text as a
legitimate location name variant from the same location-context. Using n-gram
statistics and location-related dictionaries, our Location Name Extraction tool
(LNEx) handles abbreviations and automatically filters and augments the
location names in gazetteers (handling name contractions and auxiliary
contents) to help detect the boundaries of multi-word location names and
thereby delimit them in texts.
  We evaluated our approach on 4,500 event-specific tweets from three targeted
streams to compare the performance of LNEx against that of ten state-of-the-art
taggers that rely on standard semantic, syntactic and/or orthographic features.
LNEx improved the average F-Score by 33-179%, outperforming all taggers.
Further, LNEx is capable of stream processing.
