Centre dedocumentation
scientifique

Mapping urban fingerprints of odonyms automatically extracted from French novels / Ludovic Moncla in International journal of geographical information science IJGIS, vol 33 n°12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°12 (December 2019) . - pp 2477 - 2497 Titre : Mapping urban fingerprints of odonyms automatically extracted from French novels Type de document : Article/Communication Auteurs : Ludovic Moncla, Auteur ; Mauro Gaio, Auteur ; Thierry Joliveau, Auteur ; Yves-François Le Lay, Auteur ; Pierre-Olivier Mazagol, Auteur Année de publication : 2019 Article en page(s) : pp 2477 - 2497 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] dix-neuvième siècle [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] Geoparsing [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] littérature [Termes descripteurs IGN] odonymie [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] route [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) In this paper, we propose and discuss a methodology to map the spatial fingerprints of novels and authors based on all of the named urban roads (i.e., odonyms) extracted from novels. We present several ways to explore Parisian space and fictional landscapes by interactively and simultaneously browsing geographical space and literary text. Our project involves building a platform capable of retrieving, mapping and analyzing the occurrences of named urban roads in novels in which the action occurs wholly or partly in Paris. This platform will be used in several areas, such as cultural tourism, urban research, and literary analysis. The paper focuses on extracting named urban roads and mapping the results for a sample of 31 novels published between 1800 and 1914. Two approaches to the annotation of odonyms are compared. First, we describe a proof of concept using queries made via the TXM textual analysis platform. Then, we describe an automatic process using a natural language processing (NLP) method. Additionally, we mention how the geosemantic information annotated from the text (e.g., a structure combining verbs, spatial relations, named entities, adjectives and adverbs) can be used to automatically characterize the semantic content associated with named urban roads. Numéro de notice : A2019-427 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1584804 date de publication en ligne : 17/03/2019 En ligne : https://doi.org/10.1080/13658816.2019.1584804 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93560 [article]

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements / Yingjie Hu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 714 - 738 Titre : A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements Type de document : Article/Communication Auteurs : Yingjie Hu, Auteur ; Huina Mao, Auteur ; Grant McKenzie, Auteur Année de publication : 2019 Article en page(s) : pp 714 - 738 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] publicité [Termes descripteurs IGN] recherche d'information géographique [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] répertoire toponymique [Termes descripteurs IGN] toponymie locale [Termes descripteurs IGN] traitement du langage naturel Résumé : (Auteur) Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework. Numéro de notice : A2019-213 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458986 date de publication en ligne : 13/04/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458986 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92685 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019032	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
079-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible

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]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2018031	SL	Revue	Centre de documentation	Revues en salle	Disponible

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]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2018031	SL	Revue	Centre de documentation	Revues en salle	Disponible

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]

Changement climatique et toponymie : Écologie historique du houx et du buis à travers leurs traces toponymiques / Emilien Conte (2018) 

Permalink

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

Permalink

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

Permalink

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

Permalink

Vol 17 n°1 - avril 2015 - Atles toponίmic de Catalunya nord : 1 Aiguatèbia - Montner (Bulletin de Revue Terra Nostra) / Joan Becat

Permalink

Vol 18 n°2 - avril 2015 - Atles toponίmic de Catalunya nord : 1 Montoriol - el Voló (Bulletin de Revue Terra Nostra) / Joan Becat

Permalink

Cahiers de géographie de Quebec : Toponymie (Bulletin de Cahiers de géographie du Québec) / L. Trottier

Permalink

Toponymes / Ferjan Ormeling in Cartes & Géomatique, n° 221 (septembre 2014)

Permalink

Ecriture des toponymes dans un corpus web de titres de cartes / Catherine Dominguès (2014)

Permalink

Topographic subtyping of place named entities : a linguistic approach / Van Tien Nguyen (2013)    

Permalink