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GeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)  

Public [article]  inTransactions in GIS > vol 23 n° 1 (February 2019) . - pp 118 - 136 Titre : GeoTxt: A scalable geoparsing system for unstructured text geolocation Type de document : Article/Communication Auteurs : Morteza Karimzadeh, Auteur ; Scott Pezanowski, Auteur ; Alan M. MacEachren, Auteur ; Jan Oliver Wallgrün, Auteur Année de publication : 2019 Article en page(s) : pp 118 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] analyse syntaxique [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] corpus [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] interface de programmation [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] répertoire toponymique [Termes descripteurs IGN] réseau sémantique [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) In this article, we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo‐annotated tweets. First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate. Numéro de notice : A2019-091 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12510 date de publication en ligne : 16/01/2019 En ligne : https://doi.org/10.1111/tgis.12510 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92238 [article]

Diversity and transparency in (volunteered) geoinformation practices / Tilo Felgenhauer in GI Forum, vol 2018 n° 2 ([01/09/2018])  

Public [article]  inGI Forum > vol 2018 n° 2 [01/09/2018] . - pp 97 - 102 Titre : Diversity and transparency in (volunteered) geoinformation practices Type de document : Article/Communication Auteurs : Tilo Felgenhauer, Auteur Année de publication : 2018 Article en page(s) : pp 97 - 102 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] interface utilisateur [Termes descripteurs IGN] langage cartographique [Termes descripteurs IGN] toponyme Résumé : (auteur) Geoinformationis represented in increasing numbers of different forms, with heterogeneous social and cultural implications. This paper highlights the merits as well as the ambivalence of the social (especially) and political effects of the widespread expansion and growing importance of (volunteered) geographic information. This general overview of the basic symbolic forms in which geoinformation appears distinguishes between three areas of spatial semantics: while technological systems address space mainly in numerical terms, the user refers to space by ego-centered deixis, while society and culture provide the established everyday language of toponyms. The main argument, based on this categorization, stresses the importance of diversity and transparency in acting and interacting with geoinformation and geomedia in order to promote empowerment, education and reflection. Numéro de notice : A2018-609 Thématique : GEOMATIQUE Nature : Article DOI : 10.1553/giscience2018_02_s97 En ligne : http://dx.doi.org/10.1553/giscience2018_02_s97 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92835 [article]

Are prominent mountains frequently mentioned in text? Exploring the spatial expressiveness of text frequency / Curdin Derungs in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 856 - 873 Titre : Are prominent mountains frequently mentioned in text? Exploring the spatial expressiveness of text frequency Type de document : Article/Communication Auteurs : Curdin Derungs, Auteur ; Tanja Samardžić, Auteur Année de publication : 2018 Article en page(s) : pp 856 - 873 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] Alpes centrales [Termes descripteurs IGN] échelle géographique [Termes descripteurs IGN] géomorphologie [Termes descripteurs IGN] recherche d'information géographique [Termes descripteurs IGN] Suisse [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] traitement du langage naturel Résumé : (Auteur) Data-driven GIScience shows a growing interest in making spatial information from large text data. In this paper, we quantify and thus evaluate the relation between text frequency and properties of the outer-text, geographic setting by comparing text frequencies of mountain names to the respective geomorphometric characteristics. We focus on some 2000 unique mountain names that appear some 50,000 times in a large compilation of texts on Swiss alpine history. The results on the full data set suggest only a weak relation: only 5–10% of the variation in the text frequency being explained by the respective geomorphometric characteristics. However, an analysis of multiple scales allows us to identify a Simpson’s Paradox. What appears to be ‘noise’ in the analysis of all mountains in the whole of Switzerland shows significant local signals. Small spatial extents, found all over Switzerland, can show considerably strong correlations between text frequency and spatial prominence, with up to 90% of the total variation explained. We argue that our findings have practical implications for data-driven GIScience. Retrieving meaningful spatial information from text might only be possible if the spatial scale of analysis reflects the spatial scale described in the input text documents. Numéro de notice : A2018-194 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1418362 date de publication en ligne : 26/12/2017 En ligne : https://doi.org/10.1080/13658816.2017.1418362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89859 [article]

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Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data / Kristin Stock in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 1087 - 1116 Titre : Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data Type de document : Article/Communication Auteurs : Kristin Stock, Auteur ; Javid Yousaf, Auteur Année de publication : 2018 Article en page(s) : pp 1087 - 1116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] mesure de similitude [Termes descripteurs IGN] ontologie [Termes descripteurs IGN] prise en compte du contexte [Termes descripteurs IGN] raisonnement spatial [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] traitement du langage naturel Résumé : (Auteur) Natural language descriptions of location can be complex, involving many different elements and often describing location by reference to other objects. Descriptions may be vague, and their meaning often depends upon the context within which the description has been expressed. Many previous approaches use mathematical models, focus on prepositions, and have had limited success and application. We present an approach to the interpretation of geospatial natural language expressions that uses a knowledge base of expressions for which human interpretations (in the form of degree of match to one of 50 geometric configurations) are known. Our approach interprets new expressions by finding the most similar knowledge base expression and adopting its meaning. We determine expression similarity using four different methods: element match; linguistic collocation approaches (Cosine); wordnet semantic network distance and a new approach that incorporates the contextual aspects of the expression including scale, geometry type, axial structure, image-schema and liquid/solid. As well as preposition, relatum and locatum, we consider spatial adjectives, adverbs, verb and sub-parts of the relatum and locatum. The method that incorporates context was the most successful of the four tested, selecting the same geometric configuration as human respondents in 69% of cases. Numéro de notice : A2018-200 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1432861 date de publication en ligne : 07/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1432861 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89869 [article]

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Crowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification / Benjamin Adams in Transactions in GIS, vol 22 n° 2 (April 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 2 (April 2018) . - pp 394 - 408 Titre : Crowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification Type de document : Article/Communication Auteurs : Benjamin Adams, Auteur ; Grant McKenzie, Auteur Année de publication : 2018 Article en page(s) : pp 394 - 408 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] classification [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de texte [Termes descripteurs IGN] géocodage [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] traitement du langage naturel Résumé : (Auteur) This article presents a new character‐level convolutional neural network model that can classify multilingual text written using any character set that can be encoded with UTF‐8, a standard and widely used 8‐bit character encoding. For geographic classification of text, we demonstrate that this approach is competitive with state‐of‐the‐art word‐based text classification methods. The model was tested on four crowdsourced data sets made up of Wikipedia articles, online travel blogs, Geonames toponyms, and Twitter posts. Unlike word‐based methods, which require data cleaning and pre‐processing, the proposed model works for any language without modification and with classification accuracy comparable to existing methods. Using a synthetic data set with introduced character‐level errors, we show it is more robust to noise than word‐level classification algorithms. The results indicate that UTF‐8 character‐level convolutional neural networks are a promising technique for georeferencing noisy text, such as found in colloquial social media posts and texts scanned with optical character recognition. However, word‐based methods currently require less computation time to train, so currently are preferable for classifying well‐formatted and cleaned texts in single languages. Numéro de notice : A2018-214 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12317 date de publication en ligne : 29/01/2018 En ligne : https://doi.org/10.1111/tgis.12317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90004 [article]

Désambiguïsation des entités spatiales par apprentissage actif / Amal Chihaoui in Revue internationale de géomatique, vol 28 n° 2 (avril - juin 2018)  

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Lieu et nom de lieu, du texte vers sa représentation cartographique / Catherine Dominguès (2018) 

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Toponym matching through deep neural networks / Rui Santos in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)  

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Écriture des toponymes en français : variations entre normes et usages / Catherine Dominguès in Cahiers de lexicologie, n° 110 (2017-1)

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Analyse et cartographie des sentiments dans des récits de vie de migrants / Catherine Dominguès (2017)  

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European handbook of crowdsourced geographic information, ch. 8. Quality analysis of the Parisian OSM toponyms evolution / Vyron Antoniou (2016)    

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Evaluation of NER systems for the recognition of place mentions in French thematic corpora / Carmen Brando (2016)  

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Spatial analysis of heath toponymy in relation to present-day heathland distribution / Jaime Fagundez in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)  

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Toponym recognition in custom-made map titles / Catherine Dominguès in International journal of cartography, vol 1 n° 1 (August 2015)  

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Automatic reconstruction of itineraries from descriptive texts / Ludovic Moncla (2015) 

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