Natural language Processing Introductory Knowledge _ Natural language processing

1. "The beauty of mathematics" Wu This writing is particularly vivid image, not too many formulas, popular science nature. There is a preliminary understanding of many of the technical principles of NLP. It can be said to be the best introductory reading of natural language processing. Link: https://pan.baidu.com/s/1eSphCSa Password: 59je.

2. How to make one thing for the first time in the field of NLP by Zhou Ming Microsoft Research Asia chief researcher, natural language processing top will ACL-designate, http://www.msra.cn/zh-cn/news/features/nlp-20161124

3. Deep learning Base by Chu Xipeng Fudan University August 17, 2017, 206 ppt takes you to thoroughly comb the depth learning points. http://nlp.fudan.edu.cn/xpqiu/slides/20170817-CIPS-ATT-DL.pdf,https://nndl.github.io/

4.Deep Learning for natural language processing deep learning in natural language processing by Chu Xipeng
This paper mainly discusses the application of depth learning in natural language processing. The main models involved are convolution neural network, recurrent neural network, cyclic neural network and so on, which mainly include text generation, question answering system, machine translation and text matching. Http://nlp.fudan.edu.cn/xpqiu/slides/20160618_dl4nlp@cityu.pdf

5.Deep Learning, NLP, and representations (depth learning, natural language processing and expression) from the famous Colah ' s blog, a brief overview of DL applied to NLP research, highlighting the word embeddings. http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/Translation: http://blog.csdn.net/ycheng_sjtu/article/ Details/48520293\

6. The Chinese Information Development Report by the Chinese Information Society of China December 2016 is a very good Chinese NLP overview nature of the document, through this report can understand the Chinese and English NLP main technical direction. Link: http://cips-upload.bj.bcebos.com/cips2016.pdf

7.Deep Learning in NLP (i) Word vector and language model by Lai Siwei (Come on) The Chinese Academy of Sciences Automation 2013 more detailed introduction of DL in the field of NLP research results, systematically combed a variety of neural network language models. Link: http://licstar.net/archives/328

8. Some methods of semantic analysis (one, two, three) by firelight swaying Tencent wide-point link: http://www.flickering.cn/ads/2015/02/

9. This is how we understand the language. -3 Neural network language model by the flickering of the firelight Tencent has summed up the word vector and several common neural network language models. Link: http://www.flickering.cn/nlp/2015/03/

10. Deep learning Word2vec notes based on Falao_beiliu http://blog.csdn.net/mytestmy/article/details/26961315

Application of 11.Understanding convolutional neural Networks for NLP convolution neural network in natural language processing by WILDML Link: http://www.wildml.com/2015/11/ UNDERSTANDING-CONVOLUTIONAL-NEURAL-NETWORKS-FOR-NLP Translation: http://www.csdn.net/article/2015-11-11/2826192

12.The unreasonable effectiveness of recurrent neural Networks. The amazing effectiveness of cyclic neural networks by Andrej karpathy Link: http://karpathy.github.io/2015/05/21/rnn-effectiveness/translation: https:// zhuanlan.zhihu.com/p/22107715

13.Understanding lstm Networks Understanding Long Term Memory Network (LSTM Networks) by Colah Link: http://colah.github.io/posts/2015-08- understanding-lstms/Translation: Http://www.csdn.net/article/2015-11-25/2826323?ref=myread

14. Application of attention mechanism (Attention mechanism) in natural language processing by Robert_ai Link: http://www.cnblogs.com/robert-dlut/p/5952032.html

15. How do beginners Consult natural language processing (NLP) field academic materials Liu Zhiyuan Links: http://blog.sina.com.cn/s/blog_574a437f01019poo.html\