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. Instead of exploiting specific features carefully optimized for
each task, our solution only uses one set of task-independent features and
internal representations learnt from unlabeled text for all tasks.Requiring no
task specific knowledge or sophisticated feature engineering, our approach gets
nearly state-of-the-art performance in all these three tagging tasks.

本研究提出了一种基于双向长短期记忆递归神经网络（BLSTM-RNN）的统一标记解决方案，可应用于各种标记任务，包括词性标注、分块和命名实体识别，该方法利用从无标记文本中学习的一组独立任务特征和内部表示，并不需要特定的任务知识或复杂的特征工程，在所有这三个标记任务中获得了几乎最先进的性能。

一个统一的标注解决方案：使用词嵌入的双向 LSTM 递归神经网络

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

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.

使用词嵌入的双向 LSTM 循环神经网络模型（BLSTM-RNN）在词性标注任务中表现出色，可以达到 97.40％的准确率，而且不需要使用形态学特征，同时具备与斯坦福词性标注器相当的性能。