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	<title>Thinknook &#187; nltk</title>
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		<title>Testing &amp; Diagnosing a Text Classification Algorithm</title>
		<link>http://thinknook.com/testing-diagnosing-a-text-classification-algorithm-2013-01-19/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=testing-diagnosing-a-text-classification-algorithm</link>
		<comments>http://thinknook.com/testing-diagnosing-a-text-classification-algorithm-2013-01-19/#comments</comments>
		<pubDate>Sat, 19 Jan 2013 17:37:26 +0000</pubDate>
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				<category><![CDATA[Classification]]></category>
		<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[accuracy]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[confusion matrix]]></category>
		<category><![CDATA[nltk]]></category>
		<category><![CDATA[precision]]></category>
		<category><![CDATA[recall]]></category>
		<category><![CDATA[text classification]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=922</guid>
		<description><![CDATA[To get something going with text (or any) classification algorithm is easy enough, all you need is an algorithm, such as Maximum Entropy or Naive Bayes, an implementation of each is available in many different flavors across various programming languages (I use NLTK on Python for text classification), and a bunch of already classified corpus data [&#8230;]]]></description>
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		<title>NLTK Megam (Maximum Entropy) Library on 64-bit Linux</title>
		<link>http://thinknook.com/nltk-megam-maximum-entropy-library-on-64-bit-linux-2012-11-27/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=nltk-megam-maximum-entropy-library-on-64-bit-linux</link>
		<comments>http://thinknook.com/nltk-megam-maximum-entropy-library-on-64-bit-linux-2012-11-27/#comments</comments>
		<pubDate>Tue, 27 Nov 2012 15:57:46 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Coding]]></category>
		<category><![CDATA[Coding Libraries]]></category>
		<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[Sentiment Analysis]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[logistic regression]]></category>
		<category><![CDATA[max ent]]></category>
		<category><![CDATA[megam]]></category>
		<category><![CDATA[natural language processing]]></category>
		<category><![CDATA[nltk]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=837</guid>
		<description><![CDATA[NLTK (Natural Language Toolkit) is a Python library that allows developers and researchers to extract information and annotations from text, and run classification algorithms such as the Naive Bayes or Maximum Entropy, as well as many other interesting Natural Language tools and processing techniques. The Maximum Entropy algorithm from NLTK comes in different flavours, this post will [&#8230;]]]></description>
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