digital-research-methods-08a-document-vectors-v03.nb

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Digital Research Methods 08A:
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Review: Representing the distribution of a term with a vector\
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We have already seen (in slide set 4B) that it is possible to use a vector to \
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The result is a long vector, where each element corresponds to the number of \
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then doesn\[CloseCurlyQuote]t appear again for a while.\
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We have also seen that it is possible to compare distributions of terms by \
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Looking at these charts of named entities from the book, we might conclude \
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otherwise don\[CloseCurlyQuote]t appear until half-way through. Red Dog \
becomes important in the final third.\
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We can use cosine distance to measure the co-occurrence of any two terms, not \
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We start with the most frequent words in the book (not including stopwords). \
Here is every word that appears more than twenty times, sorted in order of \
descending frequency.\
\>", "Text",
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Here is the lowercase bag of words on the first page, stopwords deleted\
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Cell["\<\
Since the word \[OpenCurlyDoubleQuote]agent\[CloseCurlyDoubleQuote] occurs on \
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This would obviously be pretty tedious, time consuming and error-prone to try \
to do by hand!\
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Here is a function to automate the process that we just did by hand...\
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We can compare the two vectors by eye, and see that they overlap in some \
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Once we have computed the vector representation for each page (or some other \
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This is a list of vectors. We can look up the vector for the first text block \
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Now suppose, for whatever reason, we find this text block to be interesting\
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The vector product gives us a way of measuring how similar this text block is \
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and seven with the second text block.\
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On this page Gotch is calling with information about a warhead, too.\
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It is not as obvious what this text block has in common with the one we \
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Using commands you learned in this and the previous lessons, try to answer \
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Some of these terms have the potential to cause us to rethink or refine the \
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