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{\large \bf Open-Domain Textual  Question Answering}\\[+6pt]
{\it Sanda Harabagiu and Dan Moldovan}\\
Department of Computer Science and Engineering, Southern Methodist University\\

\paragraph{Brief Description}\ \\
Question Answering (QA) is a fast growing area of research and
commercial interest. The problem of QA is to find answers to
open-domain questions by searching a large collection of
documents. Unlike Internet search engines, QA systems provide short,
relevant answers to questions.

The recent explosion of information available on the World Wide Web
makes question answering a compelling framework for finding
information that closely matches user needs. The success of QA
services, like AskJeeves serves as proof of the popularity of this
technique. Due to the fact that both questions and answers are
expressed in natural language, QA methodologies deal with language
ambiguities and incorporate NLP techniques. Several current NLP-based
technologies are able to provide the framework that approximates the
complex problem of answering questions from large collections of
texts.

Ideal QA systems should have good dialog understanding, rich knowledge
bases and quality text mining methods. They will certainly incorporate
common sense reasoning methods and use good approximations of world
knowledge.  Until we have these more advanced tools, we can
approximate QA with NLP enhancements of IR and IE techniques.

The tutorial presents the recent results in QA research and system
implementations.


\paragraph{Detailed Outline}
\begin{enumerate}
\item Introduction
  \begin{itemize}
  \item Problem definition
  \item Examples of questions and answers
  \item QA taxonomies
  \end{itemize}
\item QA system architectures
  \begin{itemize}
  \item Survey the most important system architecture features in
    TREC-8 QA (20 systems) and TREC-9 QA (28 systems)
  \item Present a generic QA system architecture
  \end{itemize}
\item  Basic QA
  \begin{itemize}
  \item Question processing
  \item Document retrieval
  \item Answer extraction
  \item Answer ranking
  \item Accuracy performance
  \end{itemize}
\item  Advanced QA
  \begin{itemize}
  \item Keyword selection 
  \item Paragraph indexing
  \item Logic prover for answer extraction 
  \item Answer correctness
  \item An introduction to answer fusion from several documents
  \item Interactive Q/A through Dialog 
  \item Time performance
  \end{itemize}

\item Open issues in QA
  \begin{itemize}
  \item Briefly survey current research issues in QA such as
          multilinguality, context, knowledge acquisition for ontology
          construction that will be incorporated into the future QA
          systems.
  \end{itemize}
\item  Concluding remarks
\end{enumerate}

\paragraph{Motivation}\ \\

Research in the area of open-domain Question Answering generates
considerable interest from both the NLP community and the end-users of
this technology.  In 1999, for the first time, National Institute of
Standards and Technology (NIST) has introduced a QA track as part of
the already established TREC competition. In 1999 there were 20
participants in the QA competition and in 2000 the number increased to
28. The participants include university research groups, national
research laboratories and small and large companies. The interest in
QA is world wide as evidenced by the international participation in
the TREC QA.

Open-domain QA is a complex application that encompasses many aspects
of NLP and AI. The current state of the art QA systems can produce
answers only to simple  questions. However, the complexity of QA
systems increases from year to year. 
This increase in complexity is paralleled by a sustained QA research
activity. 
\end{document}