Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models
This addresses location extraction from unstructured social media data for applications like event monitoring, though it is incremental as it builds on existing gazetteer and n-gram methods.
The paper tackled the problem of extracting location names from informal social media text by developing a gazetteer-based statistical language model to handle variability and boundaries, resulting in LNEx improving average F-Score by 33-179% compared to ten state-of-the-art taggers.
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.