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<?xml version="1.0"?>
<api>
  <query-continue>
    <allpages gapcontinue="Reading_Task" />
  </query-continue>
  <query>
    <pages>
      <page pageid="7794" ns="0" title="Reading List">
        <revisions>
          <rev contentformat="text/x-wiki" contentmodel="wikitext" xml:space="preserve">
[[Text-conf-list-Recommend|Recommend proceedings]]

[[Text-conf-list-GPU|GPU processing]]

[[Text-conf-list-rnng|RNNG documents]]</rev>
        </revisions>
      </page>
      <page pageid="7896" ns="0" title="Reading Paper">
        <revisions>
          <rev contentformat="text/x-wiki" contentmodel="wikitext" xml:space="preserve">==tool==
* word2vec tool
:* word vector tool for text classification, text clustering or information retrieval[http://sourceforge.net/projects/wvtool/]
:* google word2ve[http://code.google.com/p/word2vec/]
* document vector[http://radimrehurek.com/2014/12/doc2vec-tutorial/?utm_source=rss&amp;utm_medium=rss&amp;utm_campaign=doc2vec-tutorial]
:*　genSim[https://github.com/piskvorky/gensim/] new function
* Deep Learning for Java[http://deeplearning4j.org/]
:* word2vec[http://deeplearning4j.org/word2vec.html]

==QA==
[[2014-08-22-qalr]]

[[random reading]]

==NN &amp; RNN LM==
*[[2013-12-3]]
*[[2014-8-31]]
*[[2014-10-9]]
*[[Approaches to convert RNNLM to BNLM]]

==document classification==
* [[2014-9-10]]
==word vector==
* [[useful tutorial]]
* [[2014-10-20-word2vec|Learning Word Vectors for Sentiment Analysis]]
* deep learing in nlp
:* distributed representations for compositional semantics [http://arxiv.org/pdf/1411.3146.pdf]
:* Deep Learning for Natural Language Processing and Machine Translation [http://cl.naist.jp/~kevinduh/notes/cwmt14tutorial.pdf]
*Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews[http://arxiv.org/abs/1412.5335]
:*使用RNN和PV在情感分析效果不错,代码[https://github.com/mesnilgr/iclr15]

==learn report==
[[2014-10-19| basic tasks of speech processing]]
==learn process==
[[Information Retrieval]]

[[nlp class]]

[[nlp tool]]

==Some Things to remember==
*　video lectures [http://videolectures.net/]
*　free books [http://www.justfreebooks.info/]
* 推荐系统的tutorial slides [http://alex.smola.org/teaching/berkeley2012/slides/8_Recommender.pdf][http://www.slideshare.net/xamat/recommender-systems-machine-learning-summer-school-2014-cmu]
* understanding-lbfgs [http://aria42.com/blog/2014/12/understanding-lbfgs/]
* ml blog[http://www.cs.waikato.ac.nz/~bernhard/good-machine-learning-blogs.html][http://www.quora.com/What-are-the-best-machine-learning-blogs-or-resources-available]
* 公开课[http://52opencourse.com/]
* 机器学习日报[http://ml.memect.com/]
:*　包含大量的学习资源
* Advanced Machine Learning[http://www.seas.harvard.edu/courses/cs281/]</rev>
        </revisions>
      </page>
    </pages>
  </query>
</api>