Affiliations: [a] Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México, Mexico
| [b] Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional, Mexico
| [c] Institut National de Recherche en Informatique et en Automatique (INRIA), France
Correspondence:
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Corresponding author. Alexander Gelbukh. Centro de Investigación en Computación (CIC), Instituto
Politécnico Nacional, Mexico. E-mail: [email protected]. Web: www.gelbukh.com.
Abstract: We present a method for gender and language variety identification using a convolutional neural network (CNN). We compare the performance of this method with a traditional machine learning algorithm – support vector machines (SVM) trained on character n-grams (n = 3–8) and lexical features (unigrams and bigrams of words), and their combinations. We use a single multi-labeled corpus composed of news articles in different varieties of Spanish developed specifically for these tasks. We present a convolutional neural network trained on word- and sentence-level embeddings architecture that can be successfully applied to gender and language variety identification on a relatively small corpus (less than 10,000 documents). Our experiments show that the deep learning approach outperforms a traditional machine learning approach on both tasks, when named entities are present in the corpus. However, when evaluating the performance of these approaches reducing all named entities to a single symbol “NE” to avoid topic-dependent features, the drop in accuracy is higher for the deep learning approach.
Keywords: Convolutional neural networks, deep learning, author profiling, gender identification, language variety identification, machine learning, character n-grams, Spanish
