20160420.md

• 认知计算

  • http://www.huxiu.com/article/101560/1.html
  • http://www.almosthuman.cn/2015/12/25/aqxwb/
  • http://dupress.com/articles/cognitive-technologies-in-technology-sector-tmt/?icid=hp:ft:05
  • http://dupress.com/articles/what-is-cognitive-technology/
  • http://www2.deloitte.com/cn/zh/pages/technology-media-and-telecommunications/articles/tech-trends-2014.html
  • http://www2.deloitte.com/content/dam/Deloitte/cn/Documents/technology-media-telecommunications/deloitte-cn-tmt-techtrends2014-zh-020714.pdf
  • https://pdfs.semanticscholar.org/f778/4255d3ac1bad5a31b302fcccb54597aab547.pdf
  • http://cs.stackexchange.com/questions/37118/cognitive-computing-vs-artificial-intelligence
  • http://aitopics.org/topic/cognitive-science
  • http://www.research.ibm.com/cognitive-computing/#fbid=0Xbb2ZA71Ii
  • http://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf
  • http://www-01.ibm.com/software/ebusiness/jstart/downloads/MRTAWatsonHIMSS.pdf
  • https://www.academia.edu/3989848/The_Neural_Proposition_Structures_for_Cognitive_Systems

• DL&NLP

  • http://research.microsoft.com/apps/pubs/default.aspx?id=244472

  • http://www.ccf.org.cn/sites/ccf/download.jsp?file=/resources/1190201776262/2015/03/12/2.pdf

  • http://blog.csdn.net/malefactor

  • http://chuansong.me/account/gh_f93105623471

  • http://www.slideshare.net/hustwj/v076

  • http://www.hangli-hl.com/uploads/3/4/4/6/34465961/tsinghua_opportunities_and_challenges_in_deep_learning_for_information_retrieval.pdf

  • http://www.hangli-hl.com/uploads/3/4/4/6/34465961/dl4nlp_cas.pdf

  • http://research.microsoft.com/apps/pubs/default.aspx?id=232372

  • http://research.microsoft.com/pubs/232372/CIKM14_tutorial_HeGaoDeng.pdf

  • http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial

  • http://lxmls.it.pt/2014/socher-lxmls.pdf

  • http://nlp.stanford.edu/~socherr/DeepLearning-ACL2012-tutorial.pdf

  • http://nlp.stanford.edu/projects/DeepLearningInNaturalLanguageProcessing.shtml

  • http://nlp.stanford.edu/courses/NAACL2013/

  • http://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf

  • http://nlp.stanford.edu/~socherr/SocherBengioManning-DeepLearning-ACL2012-20120707-NoMargin.pdf

  • http://cs224d.stanford.edu/

  • http://www.csdn.net/article/2015-12-16/2826498

  • http://vdisk.weibo.com/s/A_pmE4iIotGf

  • www.52nlp.cn/斯坦福大学深度学习与自然语言处理第一讲引言

  • https://github.com/andrewt3000/DL4NLP/blob/master/README.md

  • http://licstar.net/archives/328

  • http://licstar.net/archives/620

  • http://licstar.net/archives/687

  • http://pan.baidu.com/s/1jGWmmZO

  • https://www.zhihu.com/question/40489869

  • https://www.zhihu.com/question/20981008

  • http://blog.csdn.net/iothouzhuo/article/details/47984857

  • http://www.slideshare.net/ssuser9cc1bd/piji-li-dltm

  • http://www.saifmohammad.com/WebDocs/emotion-survey.pdf

  • https://github.com/Lab41/sunny-side-up/wiki/Deep-Learning-Techniques-for-Sentiment-Analysis

  • http://llcao.net/cu-deeplearning15/

  • http://geek.csdn.net/news/detail/49440

  • http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/

  • https://cindyxiaoxiaoli.wordpress.com/2014/10/22/深度学习，自然语言处理，及表征方法-deep-learning-nlp-and-representations/

  • http://blog.dennybritz.com/2015/11/17/talking-to-machines-the-rise-of-conversational-interfaces-and-nlp/

  • http://nlp.stanford.edu/manning/papers/

  • http://www.boaoreview.com/plus/view.php?aid=838

  • http://nlp.stanford.edu/manning/papers/Manning-Last-Words-COLI_a_00239.pdf

  • http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00239

  • http://www.aclweb.org/anthology/J/J15/J15-4006.pdf

  • http://nlp.stanford.edu/manning/talks/AI-Computer-Forum-GloVe-NNdep.pdf

  • http://medialab.sjtu.edu.cn/maas-blog/wp-content/uploads/2015/07/Science-2015-Hirschberg-261-6.pdf

  • http://cacm.acm.org/magazines/2016/3/198856-deep-or-shallow-nlp-is-breaking-out/fulltext

  • http://u.cs.biu.ac.il/~yogo/nnlp.pdf

  • https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/

  • http://ir.hit.edu.cn/~car/talks/dl4nlp_PKU_20150506.pdf

  • http://ir.hit.edu.cn/~car/talks/cross-lingual-nlp_THU_20150416.pdf

  • http://www.weibo.com/ttarticle/p/show?id=2309403949236810084984

  • http://www.cips-cl.org/static/anthology/CCL-2014/CCL-14-044.pdf

  • http://linusp.github.io/2016/07/04/dl_and_nlp.html

  • http://blog.csdn.net/malefactor/article/details/52166235

  • https://www.quora.com/Why-are-there-so-few-machine-learning-and-natural-language-processing-startups

  • https://www.quora.com/Which-is-better-for-text-classification-CNN-or-RNN

  • https://www.quora.com/Whats-the-difference-between-distributed-and-distributional-semantic-representations

  • http://arxiv.org/abs/1503.02364

  • http://arxiv.org/abs/1506.06442

  • https://github.com/thunlp/NRE

  • http://deepdive.stanford.edu/relation_extraction

  • http://www.52cs.org/?p=557

  • http://www.36dsj.com/archives/34134

  • http://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/

    • https://github.com/oxford-cs-deepnlp-2017/lectures

• WordEmbedding

  • http://www.machinelearning.ru/wiki/images/d/db/MikolovWord2vecSlides.pdf
  • http://www.jeyzhang.com/tensorflow-learning-notes-3.html
  • http://www.cnblogs.com/rocketfan/p/4976806.html
  • http://x-algo.cn/index.php/2016/03/12/281/
  • http://x-algo.cn/index.php/2016/04/10/323/

• CNN

  • https://github.com/yoonkim/CNN_sentence
  • http://arxiv.org/abs/1504.06063
  • http://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43969.pdf
  • https://papers.nips.cc/paper/5550-convolutional-neural-network-architectures-for-matching-natural-language-sentences.pdf
  • https://pdfs.semanticscholar.org/9320/a229b450bee8384f218681634e039acd9c2f.pdf
  • http://research.microsoft.com/apps/pubs/default.aspx?id=259261
  • http://research.microsoft.com/pubs/259261/b6d786_3a1d8d785dd7487caa90d40efb1e2c46.pdf
  • http://www.aclweb.org/anthology/C14-1008
  • http://www.aclweb.org/anthology/C14-1220
  • http://alt.qcri.org/semeval2015/cdrom/pdf/SemEval079.pdf
  • http://www.cs.nyu.edu/~thien/pubs/vector15.pdf
  • http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
  • http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
  • https://arxiv.org/abs/1408.5882
  • http://arxiv.org/abs/1404.2188

• Tensor Network

  • https://arxiv.org/abs/1306.2164
  • http://cs.stanford.edu/people/danqi/papers/nips2013.pdf

• RNN

  • http://googleresearch.blogspot.co.uk/2015/11/computer-respond-to-this-email.html
  • http://www.extremetech.com/extreme/206521-thought-vectors-could-revolutionize-artificial-intelligence
  • http://colah.github.io/posts/2015-08-Understanding-LSTMs/
  • http://blog.csdn.net/ycheng_sjtu/article/details/48792467
  • http://karpathy.github.io/2015/05/21/rnn-effectiveness/
  • http://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf
  • http://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/
  • http://apaszke.github.io/lstm-explained.html
  • http://blog.csdn.net/dream_catcher_10/article/details/48462815
  • http://blog.csdn.net/Dark_Scope/article/details/47056361
  • http://blog.csdn.net/jdbc/article/details/49834537
  • http://pan.baidu.com/s/1o6s22Sy
  • http://www.neutronest.moe/2015-11-15-LSTM-survey.html
  • http://handong1587.github.io/deep_learning/2015/10/09/rnn-and-lstm.html
  • https://github.com/dennybritz/deeplearning-papernotes
  • https://arxiv.org/abs/1506.00019
  • http://www.felixgers.de/papers/phd.pdf
  • http://arxiv.org/abs/1506.03099
  • http://blog.csdn.net/han_xiaoyang/article/details/51253274
  • http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
  • http://karpathy.github.io/2015/05/21/rnn-effectiveness/
  • https://github.com/karpathy/char-rnn
  • https://github.com/zhangzibin/char-rnn-chinese
  • http://research.microsoft.com/pubs/200236/RNN4LU.pdf
  • http://www.sciencedirect.com/science/article/pii/S088523081400093X
  • http://nlp.stanford.edu/~socherr/thesis.pdf
  • http://blog.csdn.net/baidu_26550817/article/details/48541991
  • http://www.socher.org/index.php/Main/Semi-SupervisedRecursiveAutoencodersForPredictingSentimentDistributions
  • http://www.cs.toronto.edu/~graves/phd.pdf
  • http://www.cs.toronto.edu/~graves/preprint.pdf
  • http://arxiv.org/abs/1211.5063
  • http://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43905.pdf
  • http://stats.stackexchange.com/questions/153599/recurrent-vs-recursive-neural-networks-which-applies-better-for-nlp
  • https://www.quora.com/What-is-the-difference-between-recurrent-neural-networks-and-recursive-neural-networks
  • http://arxiv.org/abs/1212.0901
  • http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7179001
  • http://arxiv.org/abs/1212.0901
  • http://ace.cs.ohiou.edu/~razvan/courses/dl6890/presentations/lichen-lijie.pdf
  • http://www.voidcn.com/blog/shincling/article/p-4675949.html
  • http://www.voidcn.com/blog/shincling/article/p-3819311.html
  • http://machinelearning.wustl.edu/mlpapers/paper_files/icml2015_jozefowicz15.pdf
  • http://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43455.pdf
  • http://ace.cs.ohiou.edu/~razvan/courses/dl6890/presentations/lichen-lijie.pdf
  • https://arxiv.org/pdf/1506.02078
  • http://arxiv.org/abs/1406.1078
  • http://chuansong.me/n/464503442191
  • http://www.slideshare.net/BhaskarMitra3/recurrent-networks-and-beyond-by-tomas-mikolov
  • http://blog.csdn.net/shincling/article/details/49362161
  • https://getpocket.com/a/read/1369479560
  • http://arxiv.org/abs/1312.6026
  • http://arxiv.org/abs/1503.04069
  • http://hchandola.github.io/rnn/2015/08/19/lstm-part1.html
  • http://hchandola.github.io/rnn/theano/2015/09/28/lstm-part2.html

• NTM

  • http://gitxiv.com/posts/89PD2urojFsxwPytX/neural-turing-machine
  • http://klab.smpp.northwestern.edu/wiki/images/4/43/NTM2.pdf
  • https://www.technologyreview.com/s/532156/googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/
  • http://www.aclweb.org/anthology/D15-1166
  • https://getpocket.com/a/read/1371618607
  • http://rylanschaeffer.github.io/content/research/neural_turing_machine/main.html

• QA

  • http://arxiv.org/abs/1512.05742
  • http://arxiv.org/abs/1603.01417
  • http://www.guokr.com/article/17295/
  • http://www.guokr.com/link/122817/
  • http://www.cs.utoronto.ca/~fidler/publications_simple.html
  • http://movieqa.cs.toronto.edu/
  • http://www.cs.utoronto.ca/~fidler/publications_simple.html
  • http://chuansong.me/n/439362651366
  • http://www.cfilt.iitb.ac.in/resources/surveys/Question%20Answering%20Survey-biplab.pdf
  • www.52nlp.cn/qa问答系统中的深度学习技术实现
  • http://www.huxiu.com/article/158149/1.html

• NMT

  • https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/
  • http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:bahdanau-iclr2015.pdf
  • http://tcci.ccf.org.cn/summersch/classm/Introduction-to-smt.pdf
  • https://sites.google.com/site/acl16nmt
  • http://nlp.stanford.edu/projects/nmt/Luong-Cho-Manning-NMT-ACL2016-v4.pdf

• Reasoning

  • http://www.aaai.org/ocs/index.php/SSS/SSS15/paper/download/10281/10029
  • https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/download/5743/5927
  • http://arxiv.org/abs/1102.1808
  • http://arxiv.org/abs/1603.07704
  • http://arxiv.org/abs/1312.6192
  • http://cs.stanford.edu/people/danqi/papers/nips2013.pdf
  • https://www.quora.com/How-does-one-integrate-deep-learning-with-reasoning
  • http://blog.shakirm.com/wp-content/uploads/2015/11/CSML_BayesDeep.pdf

• Ranking

  • https://pdfs.semanticscholar.org/73d8/26d4c2363701b88e3e234fe3b8756c0f9671.pdf
  • http://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/42945.pdf
  • http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Deep_Semantic_Ranking_2015_CVPR_paper.pdf

• Watson

  • http://brenocon.com/watson_special_issue/
  • http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6177717
  • http://www.aaai.org/Magazine/Watson/watson.php
  • https://www.aaai.org/ojs/index.php/aimagazine/article/download/2303/2165
  • researcher.ibm.com/files/us-mike.barborak/rc24789.pdf
  • http://domino.research.ibm.com/library/cyberdig.nsf/papers/29303F8DC7A92FC9852579F30049ABCD/$File/rc25244.pdf
  • http://www-01.ibm.com/software/ebusiness/jstart/downloads/MRTAWatsonHIMSS.pdf
  • http://download.csdn.net/download/v_july_v/4061067
  • http://azureviolin.com/?p=116
  • http://www.cs.columbia.edu/~weiwei/workshop/slides/David_Gondek.pptx
  • http://www.cs.uky.edu/~raphael/grad/keepingCurrent/HowWatsonWorks.pdf
  • https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/projects/jobimtext/TutorialOnWatsonDarmstadt.pdf
  • https://www.icsi.berkeley.edu/icsi/sites/default/files/events/talk_20121109_gliozzo.pdf
  • http://ailab.ssu.ac.kr/rb/?r=home&m=upload&a=download&uid=579
  • http://azureviolin.com/?p=116