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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Prediction and Visual Intelligence for Security Information: The PREVISION H2020 Project Demestichas, Konstantinos; Hoang, Thi Bich Ngoc; Mothe, Josiane... In: CIRCLE 2020 ; https://hal.archives-ouvertes.fr/hal-02877780 ; CIRCLE 2020, Iván Cantador; Max Chevalier; Massimo Melucci; Josiane Mothe, Jul 2020, Samatan, France ; http://ceur-ws.org/Vol-2621/ (2020) BASE Show details 2 Location extraction from tweets Hoang, Thi Bich Ngoc; Mothe, Josiane In: ISSN: 0306-4573 ; Information Processing and Management ; https://hal.archives-ouvertes.fr/hal-02640811 ; Information Processing and Management, Elsevier, 2018, 54 (2), pp.129-144. ⟨10.1016/j.ipm.2017.11.001⟩ (2018) BASE Show details 3 Can we Predict Locations in Tweets? A Machine Learning Approach Hoang, Thi Bich Ngoc; Moriceau, Véronique; Mothe, Josiane In: ISSN: 0976-0962 ; International Journal of Computational Linguistics and Applications ; https://hal.archives-ouvertes.fr/hal-02901421 ; International Journal of Computational Linguistics and Applications, Alexander Gelbukh, 2018, 9, pp.0 (2018) Abstract: International audience ; Five hundred millions of tweets are posted daily, makingTwitter a major social media from which topical informationon 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. Location prediction is a useful preprocessing step for location extraction. We defined a number of features to represent tweets and conducted intensive evaluation ofmachine 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. Keyword: [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; Location extraction; Location prediction; Socialmedia; Tweets URL: https://hal.archives-ouvertes.fr/hal-02901421/file/hoang_26155.pdf https://hal.archives-ouvertes.fr/hal-02901421/document https://hal.archives-ouvertes.fr/hal-02901421 BASE Hide details 4 Predicting Locations in Tweets Hoang, Thi Bich Ngoc; Moriceau, Véronique; Mothe, Josiane In: CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02624131 ; CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics, Apr 2017, Budapest, Hungary (2017) BASE Show details

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