自然语言处理中的深度学习:方法及应用

Deep Learning in NLP:Methods and Applications

该文围绕特征表示和模型原理,以神经网络语言模型与词向量作为深度学习与自然语言处理结合的切入点,概述了当前主要深度神经网络的模型原理和相关应用。之后综述了当前研究人员在自然语言处理热点领域上所使用的最新深度学习方法并及所取得的成果。最后总结了深度学习方法在当前自然语言处理研究应用中所遇到的瓶颈,并对未来可能的研究重点做出展望。

With the rise of deep learning waves,the full force of deep learning methods has hit the Natural Language Process(NLP)and ushered in amazing technological advances in many different application areas of NLP.In this article,we firstly present the development history,main advantages and research situation of deep learning.Secondly,in terms of both feature representation and model theory,we introduces the neural language model and word embedding as the entry point,and present an overview of modeling and implementations of Deep Neural Network(DNN).Then we focus on the newest deep learning models with their wonderful and competitive performances related to different NLP tasks.At last,we discuss and summarize the existing problems of deep learning in NLP with the possible future directions.