dm.cs.tu-dortmund.de/mlbits/text-mining-contextual-information/
Contextual Information – Lecture Notes
\;0\;,\;0\;,\;1\;,\;0\;,\;\ldots \;,\;0)\)
President
\(=(\;0\;,\;1\;,\;0\;,\;0\;,\;0\;,\;\ldots \;,\;0)\)
Obama
\(\cdot\)
President
\(=(\;0\;,0\mkern -1.8mu\cdot \mkern -1.8mu 1 ,\;0\;,1\mkern -1.8mu\cdot \mkern [...] Consider these two sentences: 1
Cosine similarity: 0, if stop words were removed.
Want to recognize:
Obama
~
President
speaks
~
greets
press
~
media
Illinois
~
Chicago
1 Example taken from Kusner et al [...] \mkern -1.8mu 0,\;0\;,\;\ldots \;,\;0) = 0\)
Because of this, the documents are completely dissimilar (except for stopwords): \(\operatorname {sim}(\{ \texttt{Obama}, \texttt{speaks}, \texttt{press}, …
