TY - GEN
T1 - Location Name Disambiguation Exploiting Spatial Proximity and Temporal Consistency
AU - Awamura, Takashi
AU - Aramaki, Eiji
AU - Kawahara, Daisuke
AU - Shibata, Tomohide
AU - Kurohashi, Sadao
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics
PY - 2015
Y1 - 2015
N2 - As the volume of documents on the Web increases, technologies to extract useful information from them become increasingly essential. For instance, information extracted from social network services such as Twitter and Facebook is useful because it contains a lot of location-specific information. To extract such information, it is necessary to identify the location of each location-relevant expression within a document. Previous studies on location disambiguation have tackled this problem on the basis of word sense disambiguation, and did not make use of location-specific clues. In this paper, we propose a method for location disambiguation that takes advantage of the following two clues: spatial proximity and temporal consistency. We confirm the effectiveness of these clues through experiments on Twitter tweets with GPS information.
AB - As the volume of documents on the Web increases, technologies to extract useful information from them become increasingly essential. For instance, information extracted from social network services such as Twitter and Facebook is useful because it contains a lot of location-specific information. To extract such information, it is necessary to identify the location of each location-relevant expression within a document. Previous studies on location disambiguation have tackled this problem on the basis of word sense disambiguation, and did not make use of location-specific clues. In this paper, we propose a method for location disambiguation that takes advantage of the following two clues: spatial proximity and temporal consistency. We confirm the effectiveness of these clues through experiments on Twitter tweets with GPS information.
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UR - http://www.scopus.com/inward/citedby.url?scp=85060899785&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85060899785
T3 - SocialNLP 2015@NAACL - 3rd International Workshop on Natural Language Processing for Social Media, Proceedings of the Workshop
SP - 1
EP - 9
BT - SocialNLP 2015@NAACL - 3rd International Workshop on Natural Language Processing for Social Media, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 3rd Workshop on Natural Language Processing for Social Media, SocialNLP 2015, associated with NAACL 2015
Y2 - 5 June 2015
ER -