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missing file for adding bib citations

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cathywu committed Feb 6, 2012
1 parent fc14223 commit d33c6b17cea6bed08209cc33b176e898c9bc7a2d
Showing with 1,215 additions and 103 deletions.
  1. +4 −4 egpaper_final.aux
  2. +10 −0 egpaper_final.bbl
  3. +33 −44 egpaper_final.blg
  4. BIN egpaper_final.dvi
  5. +34 −46 egpaper_final.log
  6. BIN egpaper_final.pdf
  7. +2 −2 egpaper_final.tex
  8. +3 −7 egpaper_final.tex~
  9. +1,129 −0 ieee.bst
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@@ -1,10 +1,9 @@
\relax
-\citation{Authors11}
\@writefile{toc}{\contentsline {section}{\numberline {1}\hskip -1em.\nobreakspace {}Introduction}{1}}
\@writefile{toc}{\contentsline {section}{\numberline {2}\hskip -1em.\nobreakspace {}Previous Work}{1}}
\@writefile{toc}{\contentsline {section}{\numberline {3}\hskip -1em.\nobreakspace {}The User Review Domain}{1}}
-\@writefile{toc}{\contentsline {section}{\numberline {4}\hskip -1em.\nobreakspace {}Machine Learning Methods}{2}}
-\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}\hskip -1em.\nobreakspace {}The Naive Bayes Classifier}{2}}
+\@writefile{toc}{\contentsline {section}{\numberline {4}\hskip -1em.\nobreakspace {}Machine Learning Methods}{1}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}\hskip -1em.\nobreakspace {}The Naive Bayes Classifier}{1}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}\hskip -1em.\nobreakspace {}The Maximum Entropy Classifier}{2}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}\hskip -1em.\nobreakspace {}The Support Vector Machine Classifier}{2}}
\@writefile{toc}{\contentsline {section}{\numberline {5}\hskip -1em.\nobreakspace {}Experimental Setup}{3}}
@@ -20,7 +19,8 @@
\@writefile{toc}{\contentsline {subsection}{\numberline {6.9}\hskip -1em.\nobreakspace {}Majority Voting}{4}}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.10}\hskip -1em.\nobreakspace {}Neighboring Domain Data}{4}}
\bibstyle{ieee}
-\bibdata{egbib}
+\bibdata{fpbib}
+\bibcite{Martin}{1}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces 3-fold cross validation results on movie dataset. Values repesent positive, negative, or overall accuracy.}}{6}}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Test results on Yelp dataset with Naive Bayes classifier. Values repesent percent of reviews classified as positive for a given star rating.}}{7}}
\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Test results on Yelp dataset with Maximum Entropy classifier. Values repesent percent of reviews classified as positive for a given star rating.}}{7}}
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@@ -0,0 +1,10 @@
+\begin{thebibliography}{1}\itemsep=-1pt
+
+\bibitem{Martin}
+J.~M. D.~Martin, C.~Fowlkes.
+\newblock Learning to detect natural image boundaries using local brightness,
+ color, and texture cues.
+\newblock {\em IEEE Transactions on Pattern Analysis and Machine Intelligence},
+ 26(5):530--549, 2004.
+
+\end{thebibliography}
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@@ -1,56 +1,45 @@
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+The style file: ieee.bst
+Database file #1: fpbib.bib
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@@ -43,7 +43,7 @@
%%%%%%%%% ABSTRACT
\begin{abstract}
-We implement a series of classifiers (Naive Bayes, Maximum Entropy, and SVM) to distinguish positive and negative sentiment in critic and user reviews. We apply various processing methods, including negation tagging, part-of-speech tagging, and position tagging to achieve maximum accuracy. We test our classifiers on an external dataset to see how well they generalize. Finally, we use a majority-voting technique to combine classifiers and achieve accuracy of close to 90\% in 3-fold cross-validation\cite{Authors11}.
+We implement a series of classifiers (Naive Bayes, Maximum Entropy, and SVM) to distinguish positive and negative sentiment in critic and user reviews. We apply various processing methods, including negation tagging, part-of-speech tagging, and position tagging to achieve maximum accuracy. We test our classifiers on an external dataset to see how well they generalize. Finally, we use a majority-voting technique to combine classifiers and achieve accuracy of close to 90\% in 3-fold cross-validation.
\end{abstract}
%%%%%%%%% BODY TEXT
@@ -318,7 +318,7 @@ \subsection{Neighboring Domain Data}
{\small
\bibliographystyle{ieee}
-\bibliography{egbib}
+\bibliography{fpbib}
}
\end{document}
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@@ -26,9 +26,7 @@
%%%%%%%%% TITLE
\title{Sentiment Classification using Machine Learning Techniques}
-\author{Pranjal Vashaspati\\
-Institution1\\
-Institution1 address\\
+\author{Pranjal Vachaspati\\
{\tt\small pranjal@mit.edu}
% For a paper whose authors are all at the same institutiton,
% omit the following lines up until the closing ``}''.
@@ -37,8 +35,6 @@ Institution1 address\\
% To save space, use either the email address or home page, not both
\and
Cathy Wu\\
-Institution2\\
-First line of institution2 address\\
{\tt\small cathywu@mit.edu}
}
@@ -47,7 +43,7 @@ First line of institution2 address\\
%%%%%%%%% ABSTRACT
\begin{abstract}
-We implement a series of classifiers (Naive Bayes, Maximum Entropy, and SVM) to distinguish positive and negative sentiment in critic and user reviews. We apply various processing methods, including negation tagging, part-of-speech tagging, and position tagging to achieve maximum accuracy. We test our classifiers on an external dataset to see how well they generalize. Finally, we use a majority-voting technique to combine classifiers and achieve accuracy of close to 90\% in 3-fold cross-validation.
+We implement a series of classifiers (Naive Bayes, Maximum Entropy, and SVM) to distinguish positive and negative sentiment in critic and user reviews. We apply various processing methods, including negation tagging, part-of-speech tagging, and position tagging to achieve maximum accuracy. We test our classifiers on an external dataset to see how well they generalize. Finally, we use a majority-voting technique to combine classifiers and achieve accuracy of close to 90\% in 3-fold cross-validation\cite{Martin}.
\end{abstract}
%%%%%%%%% BODY TEXT
@@ -322,7 +318,7 @@ verbs & Unigrams & maximum & Presence & 0.45 & 0.45 & 0.42 & 0.38 & 0.3
{\small
\bibliographystyle{ieee}
-\bibliography{egbib}
+\bibliography{fpbib}
}
\end{document}
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