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    <p>No doubt, <em>Google News</em> may use other tricks (or even a combination thereof), but one relatively cheap trick, computationally, to infer topics from free-text would exploit the NLP notion that <strong>a word gets its meaning only when connected to other words</strong>.<br> An algorithm susceptible of discovering new topic categories from multiple documents could be outlined as follow:</p> <ul> <li>POS (part-of-speech) tag the text<br> We probably want to focus more on nouns and maybe even more so on named entities (such as <em>Obama</em> or <em>New England</em>)</li> <li>Normalize the text<br> In particular replace inflected words by their common stem. Maybe even replace some adjectives by a corresponding Named Entity (ex: Parisian ==> Paris, legal ==> law)<br> Also, remove noise words and noise expressions.</li> <li>identify some words from a list of manually maintained "current / recurring hot words" (Superbowl, Elections, scandal...)<br> This can be used in subsequent steps to provide more weight to some N-grams</li> <li>Enumerate all N-grams found in each documents (where N is 1 to say 4 or 5)<br> Be sure to count, separately, the number of occurrences of each N-gram within a given document and the number of documents which cite a given N-gram</li> <li>The most frequently cited N-grams (i.e. the ones cited in the most documents) are probably the Topics.</li> <li>Identify the existing topics (from a list of known topics)</li> <li>[optionally] Manually review the new topics</li> </ul> <p>This general recipe can also be altered to leverage other attributes of the documents and the text therein. For example the document origin (say cnn/sports vs. cnn/politics ...) can be used to select domain specific lexicons. Another example the process can more or less heavily emphasize the words/expressions from the document title (or other areas of the text with a particular mark-up).</p>
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