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dc.contributor.author | Fernández-Martínez, Nicolás José![]() |
es_ES |
dc.contributor.author | Periñán-Pascual, Carlos![]() |
es_ES |
dc.date.accessioned | 2023-10-11T18:01:57Z | |
dc.date.available | 2023-10-11T18:01:57Z | |
dc.date.issued | 2021-06 | es_ES |
dc.identifier.issn | 0717-1285 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/198018 | |
dc.description.abstract | [EN] Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets. | es_ES |
dc.description.sponsorship | Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], and the European Union's Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. We also thank Universidad de Granada for their financial support to the first author through the Becas de Iniciacion para estudiantes de Master 2018 del Plan Propio de la UGR. | es_ES |
dc.language | Inglés | es_ES |
dc.relation.ispartof | Onomázein | es_ES |
dc.rights | Reconocimiento - Sin obra derivada (by-nd) | es_ES |
dc.subject | Location detection | es_ES |
dc.subject | Location extraction | es_ES |
dc.subject | Geolocation | es_ES |
dc.subject | Tweet | es_ES |
dc.subject | Named entity recognition | es_ES |
dc.subject.classification | FILOLOGIA INGLESA | es_ES |
dc.title | LORE: a model for the detection of fine-grained locative references in tweets | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.7764/onomazein.52.11 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/101017861/EU | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RTC-2017-6389-5-AR//PLANIFICACIÓN Y GESTIÓN DE RECURSOS HÍDRICOS A PARTIR DE ANÁLISIS DE DATOS DE IOT/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.description.bibliographicCitation | Fernández-Martínez, NJ.; Periñán-Pascual, C. (2021). LORE: a model for the detection of fine-grained locative references in tweets. Onomázein. (52):195-225. https://doi.org/10.7764/onomazein.52.11 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.7764/onomazein.52.11 | es_ES |
dc.description.upvformatpinicio | 195 | es_ES |
dc.description.upvformatpfin | 225 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.issue | 52 | es_ES |
dc.relation.pasarela | S\456462 | es_ES |
dc.contributor.funder | Universidad de Granada | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | COMISION DE LAS COMUNIDADES EUROPEA | es_ES |