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

1 Nov 2015 • Peilu Wang • Yao Qian • Frank K. Soong • Lei He • Hai Zhao

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 for a unified tagging solution that can be applied to various tagging tasks including part-of-speech tagging, chunking and named entity recognition... (read more)

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