<story><title>Ask HN: Needs advice on learning NLP</title><text>I&amp;#x27;m just starting to learn NLP through book natural language processing with python.&lt;p&gt;I don&amp;#x27;t want to complete the book without knowing essential parts of the book.
It would be great if you guys can point out which one are important concepts to grasp on and thereby i can put extra effort to learn and experiment these concepts.&lt;p&gt;Looking for advice from folks who have learned the NLP concepts or have some kind of experience in NLP.&lt;p&gt;Bonus: point out sample projects to work on.</text></story><parent_chain></parent_chain><comment><author>jventura</author><text>I would suggest to start simple and manually to get some feeling for the problems in the field. No frameworks, no tools, just you and Python!&lt;p&gt;Do a simple experiment: get some texts, split words between spaces (e.g line.split(&amp;quot; &amp;quot;)) and use a dict to count the frequency of the words. Sort the words by frequency, look at them, and you will eventually reach the same conclusion as in figure 1 of the paper by Luhn when working for IBM in 1958 (&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;courses.ischool.berkeley.edu&amp;#x2F;i256&amp;#x2F;f06&amp;#x2F;papers&amp;#x2F;luhn58.pdf&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;courses.ischool.berkeley.edu&amp;#x2F;i256&amp;#x2F;f06&amp;#x2F;papers&amp;#x2F;luhn58.p...&lt;/a&gt;)&lt;p&gt;There are lots of corpora out there in the wild, but if you need to roll your own from wikipedia texts you can use this tool I did: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;joaoventura&amp;#x2F;WikiCorpusExtractor&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;joaoventura&amp;#x2F;WikiCorpusExtractor&lt;/a&gt;&lt;p&gt;From this experiment, and depending if you like statistics or not, you can play a bit with the numbers. For instance, you can use Tf-Idf (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Tf%E2%80%93idf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Tf%E2%80%93idf&lt;/a&gt;) to extract potential keywords from documents. Check the formula, it only uses the frequency of occurrence of words in documents.&lt;p&gt;Only use tools such as Deep neural networks if you decide later that they are essential for what you need. I did an entire PhD on this area just with Python and playing with frequencies, no frameworks at all (an eg. of my work can be found at &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.sciencedirect.com&amp;#x2F;science&amp;#x2F;article&amp;#x2F;pii&amp;#x2F;S1877050912001251&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.sciencedirect.com&amp;#x2F;science&amp;#x2F;article&amp;#x2F;pii&amp;#x2F;S1877050912...&lt;/a&gt;).&lt;p&gt;Good luck!</text></comment>
<story><title>Ask HN: Needs advice on learning NLP</title><text>I&amp;#x27;m just starting to learn NLP through book natural language processing with python.&lt;p&gt;I don&amp;#x27;t want to complete the book without knowing essential parts of the book.
It would be great if you guys can point out which one are important concepts to grasp on and thereby i can put extra effort to learn and experiment these concepts.&lt;p&gt;Looking for advice from folks who have learned the NLP concepts or have some kind of experience in NLP.&lt;p&gt;Bonus: point out sample projects to work on.</text></story><parent_chain></parent_chain><comment><author>hiddencost</author><text>NLP for what purpose?&lt;p&gt;- Academic
-- want results? deep learning [0], data munging [1,2]
-- want to understand &amp;quot;why&amp;quot; &amp;#x2F; context? Jurafsky and Martin [1]&lt;p&gt;- Professional
-- the data is easy to get and clean? deep learning [0]
-- you need to do a lot of work to get the signal? [2]&lt;p&gt;- Personal
-- &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;karpathy.github.io&amp;#x2F;2015&amp;#x2F;05&amp;#x2F;21&amp;#x2F;rnn-effectiveness&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;karpathy.github.io&amp;#x2F;2015&amp;#x2F;05&amp;#x2F;21&amp;#x2F;rnn-effectiveness&amp;#x2F;&lt;/a&gt;
-- &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;colah.github.io&amp;#x2F;posts&amp;#x2F;2014-07-NLP-RNNs-Representations&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;colah.github.io&amp;#x2F;posts&amp;#x2F;2014-07-NLP-RNNs-Representation...&lt;/a&gt;&lt;p&gt;(Andrej Karpathy and Chris Olah are some of my favorite writers)&lt;p&gt;[0] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.deeplearningbook.org&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.deeplearningbook.org&amp;#x2F;&lt;/a&gt;
[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;web.stanford.edu&amp;#x2F;~jurafsky&amp;#x2F;slp3&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;web.stanford.edu&amp;#x2F;~jurafsky&amp;#x2F;slp3&amp;#x2F;&lt;/a&gt;
[2] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;nlp.stanford.edu&amp;#x2F;IR-book&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;nlp.stanford.edu&amp;#x2F;IR-book&amp;#x2F;&lt;/a&gt;</text></comment>