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Predicting Locations in Tweets

Hoang, Thi Bich Ngoc and Moriceau, Véronique and Mothe, Josiane Predicting Locations in Tweets. (2017) In: CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics, 17 April 2017 - 23 April 2017 (Budapest, Hungary). (Unpublished)

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Five hundred millions of tweets are posted daily, making Twitter a major social media from which topical information on events can be extracted. Events are represented by time, location and entity-related information. This paper focuses on location which is an important clue for both users and geo-spatial applications. We address the problem of predicting whether a tweet contains a location or not, as location prediction is a useful pre-processing step for location extraction, by defining a number of features to represent tweets and conducting intensive evaluation of machine learning parameters. We found that: (1) not only words appearing in a geography gazetteer are important but the occurrence of a preposition right before a proper noun also is. (2) it is possible to improve precision on location extraction if the occurrence of a location is predicted.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-02624131
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > University of Economics – The University of Danang (VIETNAM)
Other partners > Université Paris-Sud 11 (FRANCE)
Laboratory name:
Deposited On:18 May 2020 09:44

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