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

Peilu Wang, Yao Qian, Frank K. Soong, Lei He, Hai Zhao
Shanghai Jiao Tong University, Shanghai, China
arXiv:1510.06168 [cs.CL], (21 Oct 2015)

@article{wang2015partofspeech,

   title={Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network},

   author={Wang, Peilu and Qian, Yao and Soong, Frank K. and He, Lei and Zhao, Hai},

   year={2015},

   month={oct},

   archivePrefix={"arXiv"},

   primaryClass={cs.CL}

}

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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.
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