Part-of-speech tagging with recurrent neural networks

Explores the use of discrete-time recurrent neural networks for part-of-speech disambiguation of textual corpora. Our approach does not need a hand-tagged text for training the tagger, being probably the first neural approach doing so. Preliminary results show that the performance of this approach is, at least, similar to that of a standard hidden Markov model trained using the Baum-Welch algorithm.