{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-low-dimensionality-representation-for","title":"A Low Dimensionality Representation for Language Variety Identification","arxiv_id":"1705.10754","date":"2017-05-30","proceeding":null,"authors":["Francisco Rangel","Marc Franco-Salvador","Paolo Rosso"],"abstract":"Language variety identification aims at labelling texts in a native language\n(e.g. Spanish, Portuguese, English) with its specific variation (e.g.\nArgentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work\nwe propose a low dimensionality representation (LDR) to address this task with\nfive different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain.\nWe compare our LDR method with common state-of-the-art representations and show\nan increase in accuracy of ~35%. Furthermore, we compare LDR with two reference\ndistributed representation models. Experimental results show competitive\nperformance while dramatically reducing the dimensionality --and increasing the\nbig data suitability-- to only 6 features per variety. Additionally, we analyse\nthe behaviour of the employed machine learning algorithms and the most\ndiscriminating features. Finally, we employ an alternative dataset to test the\nrobustness of our low dimensionality representation with another set of similar\nlanguages.","url_abs":"http://arxiv.org/abs/1705.10754v1","url_pdf":"http://arxiv.org/pdf/1705.10754v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-low-dimensionality-representation-for","repo_url":"https://github.com/HamedBabaei/ML992","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}