Using Torch7, we built a recurrent neural network part-of-speech tagger that used distributed word vectors as inputs. Our model outperformed a standard hidden Markov model and almost matched an HMM with human designed features. We designed the model, implemented the recurrent portions, tested against the part-of-speech tagging data set used in our earlier assignments and wrote a paper explaining our results.
Download our paper.