A Low Dimensionality Representationfor Language Variety IdentificationThe work of the first author was in the framework of ECOPORTUNITY IPT-2012-1220-430000. The work of the last two authors was in the framework of the SomEMBED MINECO TIN2015-71147-C2-1-P research project. This work has been also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030)
Table 9: Accuracy results in the development set of the DSLCC. The significance is marked in bold when some representation obtains significantly better results than the next best performing representation (e.g. results for SenVec in Portugal Portuguese are significantly higher than LDR, which at the same time are significantly higher than Skip-gram).
['[BOLD] Language', '[BOLD] LDR', '[BOLD] Skip-gram', '[BOLD] SenVec']
The corpus consists of 2,000 sentences per language or variety, with between 20 and 100 tokens per sentence, obtained from news headers. It is important to notice that, in general, when a particular representation improves for one language is at cost of the other one. We can conclude that the three representations obtained comparative results and support the robustness of the low dimensionality representation.