7 May
2006
7 May
'06
6 p.m.
Last minute comments?
In recent years, political methodologists, have produced innumerable automated
document ranking and classification systems. Many of these ignore word sequence
information, treating entire documents as mere collections of words. A subset of
these, including those based on the well-known Naive Bayes algorithm, assume
that word frequencies are unrelated and that word sequence information is
unimportant \cite{domingos96}. A recently developed algorithm known as
Wordscores makes an even wider set of assumptions \cite{wordscores2003}. In
this paper, we compare Wordscores to several more moderate document ranking,
classification, and summarization algorithms. Surprisingly, we find that
Wordscores shows remarkably improved performance at carrying out a small number
of carefully selected classifications on meticuously arranged collections of
political documents, demonstrate its performance at gauging the effects of news
headlines on S\&P 500 daily securities prices, and discuss its utility in other
applications.
7 May
7 May
6:02 p.m.
That was supposed to be this:
In recent years, political methodologists, have produced innumerable automated
document ranking and classification systems. Many of these ignore word sequence
information, treating entire documents as mere collections of words. A subset of
these, including those based on the well-known Naive Bayes algorithm, assume
that word frequencies are unrelated as well \cite{domingos96}. A recently
developed algorithm known as Wordscores makes an even wider set of assumptions
\cite{wordscores2003}. In this paper, we compare Wordscores to several more
moderate document ranking, classification, and summarization algorithms.
Surprisingly, we find that Wordscores shows remarkably improved performance at
carrying out a small number of carefully selected classifications on
meticuously arranged collections of political documents, demonstrate its
performance at gauging the effects of news headlines on S\&P 500 daily
securities prices, and discuss its utility in other applications.
Quoting ghumphr(a)fas.harvard.edu:
Last minute comments?
In recent years, political methodologists, have produced innumerable
automated
document ranking and classification systems. Many of these ignore word
sequence
information, treating entire documents as mere collections of words. A subset
of
these, including those based on the well-known Naive Bayes algorithm, assume
that word frequencies are unrelated and that word sequence information is
unimportant \cite{domingos96}. A recently developed algorithm known as
Wordscores makes an even wider set of assumptions \cite{wordscores2003}. In
this paper, we compare Wordscores to several more moderate document ranking,
classification, and summarization algorithms. Surprisingly, we find that
Wordscores shows remarkably improved performance at carrying out a small
number
of carefully selected classifications on meticuously arranged collections of
political documents, demonstrate its performance at gauging the effects of
news
headlines on S\&P 500 daily securities prices, and discuss its utility in
other
applications.
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6:40 p.m.
On Sun, 7 May 2006, ghumphr(a)fas.harvard.edu wrote:
Last minute comments?
you need a title
In recent years, political methodologists, have produced innumerable
innumerable? i don't know of any. tho there are some applications,
including wordscores.
automated document ranking and classification systems.
Many of these
ignore word sequence information, treating entire documents as mere
collections of words. A subset of these, including those based on the
well-known Naive Bayes algorithm, assume that word frequencies are
at least in political science, the 'naive bayes algorithm' is not
well-known. this terminology sound like computer science, not the
statistical terminology used in the soc sci's.
unrelated and that word sequence information is
unimportant
\cite{domingos96}. A recently developed algorithm known as Wordscores
makes an even wider set of assumptions \cite{wordscores2003}. In this
paper, we compare Wordscores to several more moderate document ranking,
classification, and summarization algorithms. Surprisingly, we find that
Wordscores shows remarkably improved performance at carrying out a small
number of carefully selected classifications on meticuously arranged
collections of political documents, demonstrate its performance at
gauging the effects of news headlines on S\&P 500 daily securities
prices, and discuss its utility in other applications.
i'd have thought that wordscores would be a good first attempt but one
that you could do much better than. if your results are right, it would
be good to explain why you're getting the results you are. i.e., the
results are interesting and surprising but explaining the surprise is good
too.
Gary
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