Abstract
Language variety identification aims at labelling texts in a native language (e.g. Spanish, Portuguese, English) with its specific variation (e.g. Argentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work we propose a low dimensionality representation (LDR) to address this task with five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain. We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of \({\sim }\)35%. Furthermore, we compare LDR with two reference distributed representation models. Experimental results show competitive performance while dramatically reducing the dimensionality—and increasing the big data suitability—to only 6 features per variety. Additionally, we analyse the behaviour of the employed machine learning algorithms and the most discriminating features. Finally, we employ an alternative dataset to test the robustness of our low dimensionality representation with another set of similar languages.
The 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).
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Notes
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It is important to highlight the importance of this aspect from an evaluation perspective in an author profiling scenario. In fact, if texts from the same authors are both part of the training and test sets, their particular style and vocabulary choice may contribute at training time to learn the profile of the authors. In consequence, over-fitting would be biasing the results.
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Our hypothesis is that the distribution of weights for a given document should be closer to the weights of its corresponding language variety, therefore, we use the most common descriptive statistics to measure this variability among language varieties.
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Using the LDR a document is represented by a total set of features equal to 6 multiplied by the number of categories (the 5 language varieties), in our case 30 features. This is a considerable dimensionality reduction that may be helpful to deal with big data environments.
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The HispaBlogs dataset was collected by experts on social media from the Autoritas Consulting company (http://www.autoritas.net). Autoritas experts in the different countries selected popular bloggers related to politics, online marketing, technology or trends. The HispaBlogs dataset is publicly available at: https://github.com/autoritas/RD-Lab/tree/master/data/HispaBlogs.
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We used 300-dimensional vectors, context windows of size 10, and 20 negative words for each sample. We preprocessed the text with word lowercase, tokenization, removing the words of length one, and with phrase detection using word2vec tools: https://code.google.com/p/word2vec/.
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We used SVM with default parameters and exhaustive correction code to transform the multiclass problem into a binary one.
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Rangel, F., Franco-Salvador, M., Rosso, P. (2018). A Low Dimensionality Representation for Language Variety Identification. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_13
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