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Toponym recognition in custom-made map titles / Catherine Dominguès in International journal of cartography, vol 1 n° 1 (August 2015)
[article]
Titre : Toponym recognition in custom-made map titles Type de document : Article/Communication Auteurs : Catherine Dominguès , Auteur ; Iris Eshkol-Taravella, Auteur Année de publication : 2015 Article en page(s) : pp 109 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte topographique
[Termes IGN] corpus
[Termes IGN] géobalise
[Termes IGN] prise en compte du contexte
[Termes IGN] répertoire toponymique
[Termes IGN] toponyme
[Termes IGN] traitement du langage naturelMots-clés libres : toponym subjective toponym web corpus natural language processing gazetteer Résumé : (auteur) The titles of customized topographic maps constitute a specific corpus which is characterized by a very significant number of place names and spelling variations. This paper is about identifying toponyms in these titles. The toponym tracking is based on gazetteers as well as light parsing according to patterns. The method used broadens the definition of the toponym to include the nature of the corpus and the data in it. It consists of seven successive stages where both the extralinguistic context – in this case toponym georeferencing – and the linguistic context are taken into account. Mistakes in tagging are analyzed from the corpus characteristics and the results of each step tagging are evaluated (recall, precision, F-measure). Different conclusions can be suggested: (i) toponym recognition in web corpora should take into account spelling changes, (ii) toponym recognition cannot be limited to gazetteer proper nouns, (iii) the notion of subjective toponym is relevant in this specific corpus, and could be considered with reference to the customization of maps. Numéro de notice : A2015-404 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE/TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2015.1055935 Date de publication en ligne : 07/08/2015 En ligne : https://doi.org/10.1080/23729333.2015.1055935 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76891
in International journal of cartography > vol 1 n° 1 (August 2015) . - pp 109 - 120[article]Recommendations in location-based social networks: a survey / Jie Bao in Geoinformatica, vol 19 n° 3 (July - September 2015)
[article]
Titre : Recommendations in location-based social networks: a survey Type de document : Article/Communication Auteurs : Jie Bao, Auteur ; David Wilkie, Auteur ; Mohamed Mokbe, Auteur Année de publication : 2015 Article en page(s) : pp 525 - 565 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse comparative
[Termes IGN] analyse de données
[Termes IGN] géobalise
[Termes IGN] performance
[Termes IGN] positionnement automatique
[Termes IGN] réseau social géodépendant
[Termes IGN] source de données
[Termes IGN] système de recommandation
[Termes IGN] utilisateurRésumé : (auteur) Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users’ preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users’ travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme. Numéro de notice : A2015-497 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0220-8 Date de publication en ligne : 06/02/2015 En ligne : https://doi.org/10.1007/s10707-014-0220-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77309
in Geoinformatica > vol 19 n° 3 (July - September 2015) . - pp 525 - 565[article]
Titre : Confounds and Consequences in Geotagged Twitter Data Type de document : Article/Communication Auteurs : Umashanthi Pavalanathan, Auteur ; Jacob Eisenstein, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 7/06/2015 Importance : 10 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] erreur systématique
[Termes IGN] géobalise
[Termes IGN] traitement du langage naturelRésumé : (auteur) Twitter is often used in quantitative studies that identify geographically-preferred topics, writing styles, and entities. These studies rely on either GPS coordinates attached to individual messages, or on the user-supplied location field in each profile. In this paper, we compare these data acquisition techniques and quantify the biases that they introduce; we also measure their effects on linguistic analysis and text-based geolocation. GPS-tagging and self-reported locations yield measurably different corpora, and these linguistic differences are partially attributable to differences in dataset composition by age and gender. Using a latent variable model to induce age and gender, we show how these demographic variables interact with geography to affect language use. We also show that the accuracy of text-based geolocation varies with population demographics, giving the best results for men above the age of 40. Numéro de notice : P2015-001 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.1506.02275 En ligne : https://doi.org/10.48550/arXiv.1506.02275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79901 Documents numériques
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Confounds and Consequences in Geotagged Twitter DataAdobe Acrobat PDF Mining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference / Jun Li in Transactions in GIS, vol 19 n° 1 (February 2015)
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Titre : Mining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Qiming Qin, Auteur ; Jiawei Han, Auteur ; Lu-An Tang, Auteur ; Kin Hou Lei, Auteur Année de publication : 2015 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données routières
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données localisées des bénévoles
[Termes IGN] exploration de données géographiques
[Termes IGN] géobalise
[Termes IGN] inférence
[Termes IGN] mise à jour de base de données
[Termes IGN] traitement du langage naturelRésumé : (auteur) As mapping is costly and labor-intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC-based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps. Numéro de notice : A2015--118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12072 Date de publication en ligne : 15/01/2014 En ligne : http://doi.wiley.com/10.1111/tgis.12072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102453
in Transactions in GIS > vol 19 n° 1 (February 2015) . - pp 1 - 18[article]Efficient continuous top-k spatial keyword queries on road networks / Long Guo in Geoinformatica, vol 19 n° 1 (January - March 2015)
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Titre : Efficient continuous top-k spatial keyword queries on road networks Type de document : Article/Communication Auteurs : Long Guo, Auteur ; Jie Shao, Auteur ; Htoo Htet Aung, Auteur ; Kian-Lee Tan, Auteur Année de publication : 2015 Article en page(s) : pp 29 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] espace euclidien
[Termes IGN] extraction de données
[Termes IGN] géobalise
[Termes IGN] géopositionnement
[Termes IGN] requête spatiale
[Termes IGN] réseau routier
[Termes IGN] système d'information géographique
[Termes IGN] traitement de données localiséesRésumé : (auteur) With the development of GPS-enabled mobile devices, more and more pieces of information on the web are geotagged. Spatial keyword queries, which consider both spatial locations and textual descriptions to find objects of interest, adapt well to this trend. Therefore, a considerable number of studies have focused on the interesting problem of efficiently processing spatial keyword queries. However, most of them assume Euclidean space or examine a single snapshot query only. This paper investigates a novel problem, namely, continuous top-k spatial keyword queries on road networks, for the first time. We propose two methods that can monitor such moving queries in an incremental manner and reduce repetitive traversing of network edges for better performance. Experimental evaluation using large real datasets demonstrates that the proposed methods both outperform baseline methods significantly. Discussion about the parameters affecting the efficiency of the two methods is also presented to reveal their relative advantages. Numéro de notice : A2015-485 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0204-8 En ligne : https://doi.org/10.1007/s10707-014-0204-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77248
in Geoinformatica > vol 19 n° 1 (January - March 2015) . - pp 29 - 60[article]Three dimensional volunteered geographic information: A prototype of a social virtual globe / Maria Antonia Brovelli in International journal of 3-D information modeling, vol 3 n° 2 (April - June 2014)PermalinkGeo-tagged Twitter collection and visualization system / Hideyuki Fujita in Cartography and Geographic Information Science, vol 40 n° 3 (June 2013)PermalinkUsing reverse viewshed analysis to assess the location correctness of visually generated VGI / Hansi Senaratne in Transactions in GIS, vol 17 n° 3 (June 2013)PermalinkBeyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb / Jeremy W. Crampton in Cartography and Geographic Information Science, vol 40 n° 2 (March 2013)PermalinkGeotagging is here to stay / Anonyme in GEO: Geoconnexion international, vol 9 n° 2 (february 2010)PermalinkEvery thing will be geo-tagged: local search media become social and mobile in 2010 finally / F. Fischer in Geoinformatics, vol 13 n° 1 (01/01/2010)PermalinkLe "geotagging" en toute confiance / Anonyme in Géomatique expert, n° 71 (octobre - novembre 2009)PermalinkPermalink