Centre dedocumentation
scientifique

Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? / Oliver Lock in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 275 - 292 Titre : Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? Type de document : Article/Communication Auteurs : Oliver Lock, Auteur ; Chris Pettit, Auteur Année de publication : 2020 Article en page(s) : pp 275 - 292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] artefact [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] planification urbaine [Termes IGN] réseau social [Termes IGN] sentiment [Termes IGN] Sydney (Nouvelle-Galles du Sud) [Termes IGN] traitement du langage naturel [Termes IGN] transport public [Termes IGN] ville intelligente Résumé : (auteur) We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion. Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city. With such pressures on existing public transportation systems, this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services. This research forms a case study of the use of passively collected forms of big data in cities – focusing on Sydney, Australia. Firstly, it examines social media data (Tweets) related to public transport performance. Secondly, it joins this to longitudinal big data – delay information continuously broadcast by the network over a year, thus forming hundreds of millions of data artifacts. Topics, tones, and sentiment are modeled using machine learning and Natural Language Processing (NLP) techniques. These resulting data, and models, are compared to opinions derived from a citizen survey among users. The validity of such data and models versus the intentions of users, in the context of systems that monitor and improve transport performance, are discussed. As such, key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques. Numéro de notice : A2020-787 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1815596 date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.1080/10095020.2020.1815596 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96545 [article]

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 IGN] appariement d'adresses [Termes IGN] appariement sémantique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] géocodage par adresse postale [Termes IGN] gestion urbaine [Termes IGN] inférence sémantique [Termes IGN] repésentation vectorielle [Termes IGN] réseau neuronal profond [Termes IGN] Shenzhen [Termes IGN] similitude sémantique [Termes 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]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020031	SL	Revue	Centre de documentation	Revues en salle	Disponible

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 IGN] analyse de groupement [Termes IGN] données massives [Termes IGN] espace urbain [Termes IGN] extraction de données [Termes IGN] gestion urbaine [Termes IGN] image à haute résolution [Termes IGN] point d'intérêt [Termes IGN] regroupement de données [Termes IGN] télédétection spatiale [Termes IGN] traitement du langage naturel [Termes IGN] Wuhan (Chine) [Termes 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 Editeur : Göttingen : Copernicus publications Année de publication : 2020 Collection : AGILE GIScience Series num. vol 1 Projets : 1-Pas de projet / Conférence : AGILE 2020, 23rd AGILE Conference on Geographic Information Science 16/06/2020 19/06/2020 Chania - Crète Grèce Open Access Proceedings Importance : 18 p. Format : 21 x 30 cm Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse comparative [Termes IGN] entité géographique [Termes IGN] recherche d'information géographique [Termes IGN] reconnaissance de noms [Termes 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 : UGE-LaSTIG+Ext (2020- ) 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

Extraction de connaissances pour la description de l'environnement maritime côtier à partir de textes d'aide à la navigation / Léa Lamotte in Revue des Nouvelles Technologies de l'Information, E.36 (2020)

Public [article]  inRevue des Nouvelles Technologies de l'Information > E.36 (2020) . - pp 341 - 348 Titre : Extraction de connaissances pour la description de l'environnement maritime côtier à partir de textes d'aide à la navigation Type de document : Article/Communication Auteurs : Léa Lamotte, Auteur ; Nathalie Abadie , Auteur ; Eric Saux, Auteur ; Eric Kergosien, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : EGC 2020, 20e conférence internationale francophone Extraction et Gestion des Connaissances 27/01/2020 31/01/2020 Bruxelles Belgique Article en page(s) : pp 341 - 348 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] découverte de connaissances [Termes IGN] exploration de texte [Termes IGN] information géographique [Termes IGN] littoral [Termes IGN] traitement du langage naturel Résumé : (auteur) Les référentiels de données géoréférencées sont de plus en plus utilisés pour permettre l'annotation spatiale de documents textuels et ainsi faciliter l'accès à leur contenu, voire son analyse spatiale. En revanche, peu de travaux se sont intéressés à l'extraction d'information géographique à partir de textes pour alimenter de tels référentiels. Le travail présenté dans cet article explore les potentialités de l'extraction d'information spatiale indirecte (noms de lieux, relations spatiales, etc.) dans les textes des Instructions Nautiques produites par le Service Hydrographique et Océanographique de la Marine (SHOM). La méthode proposée combine une approche lexicale et une approche à base de patrons linguistiques, puis est comparée aux principales approches d'extraction d'information géographique en français. Numéro de notice : A2020-571 Affiliation des auteurs : LaSTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96012 [article]

Representation learning for natural language processing / Zhiyuan Liu (2020)  

Permalink

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)  

Permalink

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)  

Permalink

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

Permalink

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

Permalink

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

Permalink

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)  

Permalink

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

Permalink

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)  

Permalink

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)  

Permalink