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NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages / Jimin Wang in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 719 - 735 Titre : NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages Type de document : Article/Communication Auteurs : Jimin Wang, Auteur ; Yingjie Hu, Auteur ; Kenneth Joseph, Auteur Année de publication : 2020 Article en page(s) : pp 719 - 735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] flux de travaux [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] précision sémantique [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] toponyme Résumé : (auteur) Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models. Numéro de notice : A2020-445 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12627 date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1111/tgis.12627 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95508 [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 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 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]

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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]

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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Linking spatial named entities to the web of data for geographical analysis of historical texts / Pierre-Henri Paris in Journal of Map & Geography Libraries, vol 13 n° 1 ([01/05/2017])  

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