Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network

Understand

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.