Peilu Wang

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

2015 · arXiv (Cornell University)

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 …

A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding

2015 · arXiv (Cornell University)

Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or handwritten documents. In this study, we propose to use BLSTM-RNN …