Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models

Hussein Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth


Abstract
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.
Anthology ID:
C18-1169
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1986–1997
Language:
URL:
https://aclanthology.org/C18-1169
DOI:
Bibkey:
Cite (ACL):
Hussein Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, and Amit Sheth. 2018. Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1986–1997, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models (Al-Olimat et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1169.pdf
Code
 halolimat/LNEx