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dc.contributor.authorDonoso, Gonzaloes_ES
dc.contributor.authorSánchez, Davides_ES
dc.date.accessioned2018-12-04T08:47:43Z-
dc.date.available2018-12-04T08:47:43Z-
dc.date.issued2017-04-
dc.identifier.citationFourth Workshop on NLP for Similar Languages, Varieties and Dialects (2017)es_ES
dc.identifier.otherarXiv:1702.06777v1-
dc.identifier.urihttp://hdl.handle.net/10261/172921-
dc.descriptionTrabajo presentado en el Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial’2017), celebrado en Valencia el 3 de abril de 2017.es_ES
dc.description.abstractIn the last few years, microblogging platforms such as Twitter have given rise to a deluge of textual data that can be used for the analysis of informal communication between millions of individuals. In this work, we propose an information-theoretic approach to geographic language variation using a corpus based on Twitter. We test our models with tens of concepts and their associated keywords detected in Spanish tweets geolocated in Spain. We employ dialectometric measures (cosine similarity and Jensen-Shannon divergence) to quantify the linguistic distance on the lexical level between cells created in a uniform grid over the map. This can be done for a single concept or in the general case taking into account an average of the considered variants. The latter permits an analysis of the dialects that naturally emerge from the data. Interestingly, our results reveal the existence of two dialect macrovarieties. The first group includes a region-specific speech spoken in small towns and rural areas whereas the second cluster encompasses cities that tend to use a more uniform variety. Since the results obtained with the two different metrics qualitatively agree, our work suggests that social media corpora can be efficiently used for dialectometric analyses.es_ES
dc.language.isoenges_ES
dc.relation.isversionofPreprint-
dc.rightsopenAccesses_ES
dc.titleDialectometric analysis of language variation in Twitteres_ES
dc.typecomunicación de congresoes_ES
dc.description.peerreviewedNoes_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
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