  Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has
been shown to be very effective for tagging sequential data, e.g. speech
utterances or handwritten documents. While word embedding has been demoed as a
powerful representation for characterizing the statistical properties of
natural language. In this study, we propose to use BLSTM-RNN with word
embedding for part-of-speech (POS) tagging task. When tested on Penn Treebank
WSJ test set, a state-of-the-art performance of 97.40 tagging accuracy is
achieved. Without using morphological features, this approach can also achieve
a good performance comparable with the Stanford POS tagger.
