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		<title>Word2vec with gensim &#8211; a simple word embedding example</title>
		<link>https://petaminds.com/word2vec-with-gensim-a-simple-word-embedding-example/</link>
					<comments>https://petaminds.com/word2vec-with-gensim-a-simple-word-embedding-example/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Wed, 11 Apr 2018 05:58:27 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[CBOW]]></category>
		<category><![CDATA[GENSIM]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[NLP]]></category>
		<category><![CDATA[skip-grams]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1127</guid>

					<description><![CDATA[<p>In this short article, we show a simple example of how to use GenSim and word2vec for word embedding. Word2vec Word2vec is a famous algorithm for natural language processing (NLP) created by Tomas Mikolov teams. It is a group of related models that are used to produce&#160;word embeddings, i.e. CBOW and skip-grams. The models are [&#8230;]</p>
<p>The post <a href="https://petaminds.com/word2vec-with-gensim-a-simple-word-embedding-example/">Word2vec with gensim &#8211; a simple word embedding example</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
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