Traditionally, approaches based on neural networks to solve the problem of disambiguation of the meaning of words (WSD) use a set of classifiers at the end, which results in a specialization in a single set of words-those for which they were trained. This makes impossible to apply the learned models to words not previously seen in the training corpus. This paper seeks to address a generalization of the problem of WSD in order to solve it through deep neural networks without limiting the method to a fixed set of words, with a performance close to the state-of-the-art, and an acceptable computational cost. We explore different architectures based on multilayer perceptrons, recurrent cells (Long Short-Term Memory-LSTM and Gated Recurrent Units-GRU), and a classifier model. Different sources and dimensions of embeddings were tested as well. The main evaluation was performed on the Senseval 3 English Lexical Sample. To evaluate the application to an unseen set of words, learned models are evaluated in the completely unseen words of a different corpus (Senseval 2 English Lexical Sample), overcoming the random baseline.INDEX TERMS Word sense disambiguation, recurrent neural networks, LSTM, multilayer perceptron, senseval english lexical sample test.

In this work we experiment with the hypothesis that words subjects use can be used to predict their psychological attachment style (secure, fearful, dismissing, preoccupied) as defined by Bartholomew and Horowitz. In order to verify this hypothesis, we collected a series of autobiographic texts written by a set of 202 participants. Additionally, a psychological instrument (Frías questionnaire) was applied to these same participants to measure their attachment style. We identified characteristic patterns for each style of attachment by means of two approaches: (1) mapping words into a word space model composed of unigrams, bigrams and/or trigrams on which different classifiers were trained (Naïve Bayes (NB), Bernoulli NB, Multinomial NB, Multilayer Perceptrons); and (2) using a word-embedding based representation and a neural network architecture based on different units (LSTM, Gated Recurrent Units (GRU) and Bilateral GRUs). We obtained the best accuracy of 0.4079 for the first approach by using a Boolean Multinomial NB on unigrams, bigrams and trigrams altogether, and an accuracy of 0.4031 for the second approach using Bilateral GRUs.