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    <p>The algorithms you talk about are found mainly in 3 <a href="http://en.wikipedia.org/wiki/Artificial_intelligence" rel="nofollow">AI</a> branches: <a href="http://en.wikipedia.org/wiki/Natural_language_processing" rel="nofollow">NLP</a>, <a href="http://en.wikipedia.org/wiki/Machine_learning" rel="nofollow">ML</a> and <a href="http://en.wikipedia.org/wiki/Information_retrieval" rel="nofollow">IR</a>.</p> <p>For example to find the most similar 10 questions of a new question one could extract <a href="http://en.wikipedia.org/wiki/N-gram" rel="nofollow">n-grams</a> from the texts of each question, compute <a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow">TF-IDF</a> weight vectors for each question's n-grams, then compute the <a href="http://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow">cosine similarity</a> between the new question and all the other questions, and choose the 10 questions with the highest similarities.</p> <p>Some free books you can read:<br> <a href="http://nlp.stanford.edu/IR-book/" rel="nofollow">http://nlp.stanford.edu/IR-book/</a><br> <a href="http://infolab.stanford.edu/~ullman/mmds.html" rel="nofollow">http://infolab.stanford.edu/~ullman/mmds.html</a> </p> <p>And a 2 free courses starting late January:<br> <a href="http://www.nlp-class.org/" rel="nofollow">http://www.nlp-class.org/</a><br> <a href="http://jan2012.ml-class.org/" rel="nofollow">http://jan2012.ml-class.org/</a> </p> <p>Also (kind of involved):<br> <a href="http://see.stanford.edu/see/courseinfo.aspx?coll=63480b48-8819-4efd-8412-263f1a472f5a" rel="nofollow">http://see.stanford.edu/see/courseinfo.aspx?coll=63480b48-8819-4efd-8412-263f1a472f5a</a><br> <a href="http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1" rel="nofollow">http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1</a> </p>
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    1. COYou gave me some food for thought :)
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