<story><title>Deeply Moving: Deep Learning for Sentiment Analysis</title><url>http://nlp.stanford.edu/sentiment/index.html</url></story><parent_chain></parent_chain><comment><author>dave_sullivan</author><text>If you&amp;#x27;re interested in using deep learning for nlp, I highly suggest putting a pot of coffee on and watching this a couple times: &lt;a href=&quot;http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.socher.org&amp;#x2F;index.php&amp;#x2F;DeepLearningTutorial&amp;#x2F;DeepLea...&lt;/a&gt;&lt;p&gt;The really neat part is moving away from bag of words representations. Bag of words was always a bit of a hack. But seriously, watch those videos, it will give you a good idea on how it all works, the op is using similar methods.&lt;p&gt;Edit: thanks gallamine, I fixed the link</text></comment>
<story><title>Deeply Moving: Deep Learning for Sentiment Analysis</title><url>http://nlp.stanford.edu/sentiment/index.html</url></story><parent_chain></parent_chain><comment><author>agibsonccc</author><text>I know there&amp;#x27;s deep hate for java around here, but here&amp;#x27;s another relevant link. (You could probably shoehorn in scala or clojure as well) This is a direct tutorial for doing deep learning.&lt;p&gt;&lt;a href=&quot;http://nlp.stanford.edu/courses/NAACL2013/&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;nlp.stanford.edu&amp;#x2F;courses&amp;#x2F;NAACL2013&amp;#x2F;&lt;/a&gt;&lt;p&gt;Take a look at a recent homework assignment they had to do with  Named Entity Recognition and Deep Learning:
&lt;a href=&quot;http://nlp.stanford.edu/~socherr/pa4_ner.pdf&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;nlp.stanford.edu&amp;#x2F;~socherr&amp;#x2F;pa4_ner.pdf&lt;/a&gt;&lt;p&gt;It&amp;#x27;s good to see neural networks being leveraged as they are. Conditional Random Fields have been dominant for sequence based classifiers for a while. We&amp;#x27;ve been needing to push the envelope a bit further.</text></comment>