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Mining the web for points of interest
Abstract A point of interest (POI) is a focused geoA point of interest (POI) is a focused geographic entity such as a landmark, a school, an historical building, or a business. Points of interest are the basis for most of the data supporting location-based applications. In this paper we propose to curate POIs from online sources by bootstrapping training data from Web snippets, seeded by POIs gathered from social media. This large corpus is used to train a sequential tagger to recognize mentions of POIs in text. Using Wikipedia data as the training data, we can identify POIs in free text with an accuracy that is 116% better than the state of the art POI identifier in terms of precision, and 50% better in terms of recall. We show that using Foursquare and Gowalla checkins as seeds to bootstrap training data from Web snippets, we can improve precision between 16% and 52%, and recall between 48% and 187% over the state-of-the-art. The name of a POI is not sufficient, as the POI must also be associated with a set of geographic coordinates. Our method increases the number of POIs that can be localized nearly three-fold, from 134 to 395 in a sample of 400, with a median localization accuracy of less than one kilometer.ation accuracy of less than one kilometer.
Abstractsub A point of interest (POI) is a focused geoA point of interest (POI) is a focused geographic entity such as a landmark, a school, an historical building, or a business. Points of interest are the basis for most of the data supporting location-based applications. In this paper we propose to curate POIs from online sources by bootstrapping training data from Web snippets, seeded by POIs gathered from social media. This large corpus is used to train a sequential tagger to recognize mentions of POIs in text. Using Wikipedia data as the training data, we can identify POIs in free text with an accuracy that is 116% better than the state of the art POI identifier in terms of precision, and 50% better in terms of recall. We show that using Foursquare and Gowalla checkins as seeds to bootstrap training data from Web snippets, we can improve precision between 16% and 52%, and recall between 48% and 187% over the state-of-the-art. The name of a POI is not sufficient, as the POI must also be associated with a set of geographic coordinates. Our method increases the number of POIs that can be localized nearly three-fold, from 134 to 395 in a sample of 400, with a median localization accuracy of less than one kilometer.ation accuracy of less than one kilometer.
Bibtextype inproceedings  +
Doi 10.1145/2348283.2348379  +
Has author Rae A. + , Murdock V. + , Adrian Popescu + , Bouchard H. +
Has extra keyword Bootstrap training + , Free texts + , Geo-localisation + , Geographic coordinates + , Geographic information + , Historical buildings + , Localization accuracy + , Location-based applications + , Online sources + , Point of interest + , Points of interest + , Social media + , State of the art + , Training data + , Wikipedia + , Information retrieval + , Character recognition +
Has keyword Geo-localisation + , Geographic information extraction + , Location-based applications + , Points of interest +
Isbn 9781450316583  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 711–720  +
Published in SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval +
Title Mining the web for points of interest +
Type conference paper  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 8 November 2014 05:15:52  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 05:15:52  +
DateThis property is a special property in this wiki. 2012  +
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