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Im doing a Java application where I\'ll have to determine what are the Trending Topics from a specific collectio开发者_如何学编程n of tweets, obtained trough the Twitter Search. While searching in the
I\'m doing a piece of university coursework, and I\'m stuck with some Prolog. The coursework is to make a really rudimentary Watson (the machine that answers questions on Jeapoardy).
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The gale-church algorithm is available in the python-NLTK but can anyone show me an example of how to call the function within a python script? i\'m clueles开发者_Python百科s about how to do that.
Is there a way to get the subject of a sentence using OpenNLP? I\'m trying to identify the most important part of a users sentence.Generally, users will be submitting sentences to our \"engine\" and
$html=strip_tags($html); $html=ereg_replace(\"[^A-Za-zäÄÜüÖö]\",\" \",$html); $words = preg_split(\"/[\\s,]+/\", $html);
I tried google and found little that I could understand. I understand Markov chains to a very basic level: It\'s a mathematical model that only depends on previous input to change states..so sort of
I have blocks of text I want to tokenize, but I don\'t want to tokenize on whitespace and punctuation, as seems to be the standard with tools like NLTK. There are particular phrases that I want to be
I need to analyze the text to exist in it banned words. Suppose the black list is the word: \"Forbid\". The word has many forms. In the text the word can be, for example: \"forbidding\", \"forbidden\"
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