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    <p>There are several conventional techniques by which <em>words</em> are mapped to <em>features</em> (columns in a 2D data matrix in which the rows are the individual data vectors) for input to machine learning models.<a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.6513" rel="nofollow">classification</a>: </p> <ul> <li><p>a <em>Boolean</em> field which encodes the presence or absence of that word in a given document;</p></li> <li><p>a <em>frequency histogram</em> of a predetermined set of words, often the X most commonly occurring words from among all documents comprising the training data (more about this one in the last paragraph of this Answer);</p></li> <li><p>the <em>juxtaposition</em> of two or more words (e.g., 'alternative' and 'lifestyle' in consecutive order have a meaning not related either component word); this juxtaposition can either be captured in the data model itself, eg, a boolean feature that represents the presence or absence of two particular words directly adjacent to one another in a document, or this relationship can be exploited in the ML technique, as a naive Bayesian classifier would do in this instance<em>emphasized text</em>;</p></li> <li><p>words as <em>raw</em> data <em>to extract latent features</em>, eg, <a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis" rel="nofollow">LSA</a> or Latent Semantic Analysis (also sometimes called LSI for Latent Semantic Indexing). LSA is a matrix decomposition-based technique which derives latent variables from the text not apparent from the words of the text itself.</p></li> </ul> <p>A common reference data set in machine learning is comprised of frequencies of 50 or so of the most common words, aka "stop words" (e.g., <em>a</em>, <em>an</em>, <em>of</em>, <em>and</em>, <em>the</em>, <em>there</em>, <em>if</em>) for published works of Shakespeare, London, Austen, and Milton. A basic multi-layer perceptron with a single hidden layer can separate this data set with 100% accuracy. This data set and variations on it are widely available in ML Data Repositories and <a href="http://escholarship.org/uc/item/9w56v25f.pdf" rel="nofollow">academic papers</a> presenting classification results are likewise common.</p>
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