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Named entity recognition for Chinese microblog with convolutional neural network

机译:卷积神经网络的中文微博命名实体识别

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Named Entity Recognition (NER) has usually focused on traditional formal text. we consider the task of NER on microblog text. In this paper, we propose a Convolutional Neural Network for NER in Chinese microblog text. Instead of traditional machine learning needing man-made input features carefully optimized for NER task, our system learns the words feature by itself. Our network uses a sliding window of word context to predict tags. Experimental results show that our model achieved 80% accuracy on this task.
机译:命名实体识别(NER)通常侧重于传统形式文本。我们考虑了NER在微博文本上的任务。在本文中,我们为中文微博文本中的NER提出了卷积神经网络。取代传统的机器学习需要为NER任务精心优化的人工输入功能,我们的系统可自行学习单词功能。我们的网络使用单词上下文的滑动窗口来预测标签。实验结果表明,我们的模型在此任务上达到了80%的精度。

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