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A deep learning architecture for semantic address matching / Yue Lin in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 559 - 576 Titre : A deep learning architecture for semantic address matching Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Mengjun Kang, Auteur ; Yuyang Wu, Auteur Année de publication : 2020 Article en page(s) : pp 559 - 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] appariement d'adresses [Termes descripteurs IGN] appariement sémantique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] repésentation vectorielle [Termes descripteurs IGN] réseau de neurones profond [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] similitude sémantique [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set. Numéro de notice : A2020-106 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1681431 date de publication en ligne : 24/10/2019 En ligne : https://doi.org/10.1080/13658816.2019.1681431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94702 [article]

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A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 80 (March 2020) Titre : A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data Type de document : Article/Communication Auteurs : Sheng Hu, Auteur ; Zhanjun He, Auteur ; Liang Wu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] données massives [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] traitement du langage naturel [Termes descripteurs IGN] Wuhan (Chine) [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed. Numéro de notice : A2020-191 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2019.101442 date de publication en ligne : 15/11/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101442 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94853 [article]

Comparing supervised learning algorithms for Spatial Nominal Entity recognition / Amine Medad (2020)  

Public Titre : Comparing supervised learning algorithms for Spatial Nominal Entity recognition Type de document : Article/Communication Auteurs : Amine Medad, Auteur ; Mauro Gaio, Auteur ; Ludovic Moncla, Auteur ; Sébastien Mustière , Auteur ; Yannick Le Nir, Auteur Congrès : AGILE 2020, 23rd AGILE Conference on Geographic Information Science (2020), Commanditaire Editeur : Göttingen : Copernicus publications Année de publication : 2020 Collection : AGILE GIScience Series num. vol 1 Projets : 1-Pas de projet / Importance : 18 p. Format : 21 x 30 cm Note générale : biblographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme d'apprentissage [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] entité géographique [Termes descripteurs IGN] recherche d'information géographique [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) Discourse may contain both named and nominal entities. Most common nouns or nominal mentions in natural language do not have a single, simple meaning but rather a number of related meanings. This form of ambiguity led to the development of a task in natural language processing known as Word Sense Disambiguation. Recognition and categorisation of named and nominal entities is an essential step for Word Sense Disambiguation methods. Up to now, named entity recognition and categorisation systems mainly focused on the annotation, categorisation and identification of named entities. This paper focuses on the annotation and the identification of spatial nominal entities. We explore the combination of Transfer Learning principle and supervised learning algorithms, in order to build a system to detect spatial nominal entities. For this purpose, different supervised learning algorithms are evaluated with three different context sizes on two manually annotated datasets built from Wikipedia articles and hiking description texts. The studied algorithms have been selected for one or more of their specific properties potentially useful in solving our problem. The results of the first phase of experiments reveal that the selected algorithms have similar performances in terms of ability to detect spatial nominal entities. The study also confirms the importance of the size of the window to describe the context, when word-embedding principle is used to represent the semantics of each word. Numéro de notice : C2020-013 Affiliation des auteurs : UMR LaSTIG+Ext Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-1-15-2020 date de publication en ligne : 15/07/2020 En ligne : https://doi.org/10.5194/agile-giss-1-15-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95688

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 Affiliation des auteurs : non IGN 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 Affiliation des auteurs : non IGN 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]

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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)  

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Valorisation de récits de vie de Républicains espagnols / Catherine Dominguès (2019) 

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Fictive motion extraction and classification / Ekaterina Egorova in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

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Services web pour l’annotation sémantique d’information spatiale à partir de corpus textuels / Ludovic Moncla in Revue internationale de géomatique, vol 28 n° 4 (octobre - décembre 2018)  

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Extraction of spatio‐temporal data about historical events from text documents / Susanna Abraham in Transactions in GIS, vol 22 n° 3 (June 2018)  

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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)  

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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)  

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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)  

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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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Actes de la conférence TALN 2018, Volume 1. Articles longs, articles courts de TALN / Pascale Sébillot (2018)  

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