\begin{kasten}
    \section*{ \hspace{0.1cm} {\color{red} \underline{WHAT IS INFORMATION RETRIEVAL? }}}
\begin{minipage}{4cm}
\includegraphics[width=4cm]{overload.eps}
\end{minipage}
\begin{minipage}{8cm}
Information retrieval (IR):
\bi
\item
The science of searching for information/meta-data within documents.
\item
IR is interdisciplinary,
based on computer science, mathematics, library science, information
science, information architecture, cognitive psychology, linguistics,
statistics and physics.
\item
We explore IR methods since they are designed to scale to very large corpora.
More rigorous approaches require far CPU and memory and fundamentally
cannot scale to large corpora.
\ei
\end{minipage}
\end{kasten}

\begin{kasten}
    \section*{ \hspace{0.1cm} {\color{red} \underline{WHY STUDY INFORMATION RETRIEVAL?}}}
IR is the art of exploring very large collections, without first imposing complex
structure on those collections. It it therefore ideally suited to the 
overhead of an engineer building {\bf new} designs while reflecting
over {\em old} designs.
~\\

IR is the core technology used within WVU's HAMLET tool. HAMLET
is a software archaeology tool that checks new designs against
old designs:
\bi
\item
Given a partial description of the current design...
\item
... HAMLET searchers prior designs looking for items that
should be added/deleted to/from the current design.
\ei
HAMLET 
is designed to be ``glue'' that permits the searching of
technical data in large
heterogeneous collections:
\bi
\item
The tool
makes minimal assumptions about the 
nature of the engineering documents being explored.
\item
Specially, HAMLET treats all STEP documents, project memos,
spreadsheets, records in a database, in exactly the same way; namely
as a matrix of $T$ terms in $D$ documents.
\ei
\begin{center}
             \includegraphics[width=8cm]{JOMAeqn1644148.eps}
\end{center}

\end{kasten}

\begin{kasten}
    \section*{ \hspace{0.1cm} {\color{red} \underline{ONLY T*D? 
THAT CAN'T WORK (OR CAN IT?)}}}
IR works because human languages follow very simple
mathematical patterns. Zipf's Law state that the probability
of finding items at frequency $P$ follows a log-log distribution;
i.e. most things occur very rarely and only a few things occur
very often.
\begin{center}
\includegraphics[width=3in]{zipf-drupalorg_0.eps}  
\end{center}
That is, human technical documents have a very simple and regular
structure. Simple $T*D$ matrices (see $TF*IDF$) are hence often enough to record
and analyze that structure.

\end{kasten}

