A Primer on Neural Network Models for Natural Language Processing
A Primer on Neural Network Models for Natural Language Processing
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DOI：
10.1613/jair.4992
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发表时间：
2016-01-01
影响因子：
5
通讯作者：
Goldberg, Yoav
中科院分区：
文献类型：
--
作者：
Goldberg, Yoav
在过去的几年里，神经网络作为强大的机器学习模型重新出现，在图像识别和语音处理等领域取得了最先进的成果。最近，神经网络模型也开始应用于文本自然语言信号，同样取得了非常有希望的结果。本教程从自然语言处理研究的角度调查神经网络模型，试图让自然语言研究人员快速了解神经技术。本教程涵盖自然语言任务的输入编码、前馈网络、卷积网络、循环网络和递归网络，以及自动梯度计算的计算图抽象。
Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.