\documentclass{beamer}
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\usepackage{color}

\title{UNED bilingual experiments at WebCLEF}
\subtitle{Cross Language Evaluation Forum 2005}
\author{Javier Artiles, Víctor Peinado, Anselmo Peñas, Felisa Verdejo}

\institute[UNED]{NLP \& IR Group \\
\texttt{http://nlp.uned.es} \\
Dept. de Lenguajes y Sistemas Informáticos\\ 
ETSI de Informática - UNED}

\date[WebCLEF2005]{Vienna. September 23, 2005}


\begin{document}

\frame{\titlepage}

\section[Outline]{}
\frame{\tableofcontents}

\section{UNED's participation at WebCLEF 2005}
\subsection{Indexing EuroGov}

\begin{frame}
  \frametitle{Indexing EuroGov}

  Bilingual EN$\to$ES task: English topics with target webpages
  written in Spanish.

  \begin{itemize}
  \item Lucene's API.
  \item One index containing ES + UNKNOWN pages.
  \item Indexed by fields: title, metadata, headers, body\ldots
  \item No stemming or lemmatization.
  \item Tokenization with Lucene's \texttt{StandardAnalyzer}.
  \end{itemize}

\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\subsection{Translating topics}

\begin{frame}
  \frametitle{Do we have to translate or not?}

  Given a word $w$ and an index $Lang$, we decide whether to translate
  $w$ or not, by computing the word relative frequency wrt the number
  of documents in the corresponding index:

   \begin{equation}
%     F_{w_{Lang}} = \frac{wf_{Lang}}{\# docs_{Lang}}
     F(w)_{Lang} = \frac{tf(w)}{N_{Lang}}
   \end{equation}

  \begin{itemize}
  \item In the bilingual task, if $F_{w_{ES}} > F_{w_{EN}}$ then we
    assume $w$ must remain \textcolor{blue}{untranslated}.
    
    \begin{center}
      \small{Description of \textcolor{red}{Moncloa} Palace}
    \end{center}

  \item Otherwise, we attempt to lemmatize and translate it with our
    dictionary (Vox + EuroWordNet + FreeDict).
  \end{itemize}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
  \frametitle{Translating OOV words}

  \textcolor{blue}{A Spanish document containing the English word
    $w_{EN}$ might also contain its translation into Spanish} e.g.
  [Vogel et al., SIGIR05]

  \vspace{0.7cm}

  Given an English OOV word $w_{EN}$:

  \begin{enumerate}
  \item We google for Spanish pages containing $w_{EN}$.
  \item We take the 10 most frequent Spanish words (after removing
    stopwords) from the 40 first snippets retrieved by Google
    $\rightarrow$ \textcolor{blue}{candidate translations}
  \item We search for English pages containing each candidate and
    count up every time it co-occurs with $w_{EN}$ in the snippets
    $\rightarrow$ \textcolor{blue}{inverse translation}
  \item We rank the candidates and choose the most
    frequent in 2) and 3) as the ultimate translation.
  \end{enumerate}

\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
  \frametitle{Translating OOV words}

\begin{center}
  \small{Strategy for the preservation of the
    \textcolor{red}{Cantabrian} brown bear}
\end{center}

  \begin{enumerate}
  \item We google for Spanish pages containing \textit{Cantabrian}.
  \item We take the 10 most frequent words appearing in the
    snippets:
    
    \begin{center}
      \small{cantabria (16), spain (14), diccionarios (10), mountains
        (9), glosarios (6), términos (6), turismo (5), región (5), sea
        (5)}
    \end{center}

  \item We search for English pages containing each candidate and
    count up every time it co-occurs with \textit{Cantabrian}.
  \item We rank the candidates and choose \textit{cantabria} as the
    ultimate translation.
  \end{enumerate}


\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\subsection{Experiments}

\begin{frame}
  \frametitle{Experiments}

  Terms are combined with the \texttt{AND} operator in a full boolean
  query. 
 
  \begin{itemize}
  \item Baseline: search over the body field and let the search engine
    rank the results.
  \item Proposal: order the fields (title, metadata, headings, body).

  \begin{enumerate}
    \item Launch the query over \textcolor{blue}{title} fields.
    \item If we don't get 50 pages, launch the query over
      \textcolor{blue}{metadata} fields and append the results,
      removing duplicates, if any.
    \item If we don't have 50 pages, launch the query over
      \textcolor{blue}{heading} fields and append the results,
      removing duplicates, if any.
    \item \ldots 
    \end{enumerate}
  \end{itemize}

  If we cannot reach 50 results yet, we repeat the process using
  \texttt{OR}.
  
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Results}

\begin{frame}
  \frametitle{Results}

  \begin{table}
    \centering
    \begin{tabular}{l|c|c|c}
      \textbf{}& \textbf{baseline} & \textbf{proposal} & \textbf{variation} \\ \hline
      Avg success at 1    & 0.02 & 0.08 & \textcolor{blue}{+300\%} \\ 
      Avg success at 5    & 0.07 & 0.10 & \textcolor{blue}{+43\%} \\ 
      Avg success at 10   & 0.10 & 0.12 & \textcolor{blue}{+20\%} \\ 
      Avg success at 20   & 0.17 & 0.13 & \textcolor{red}{-23\%} \\ 
      Avg success at 50   & 0.26 & 0.21 & \textcolor{red}{-19\%} \\ \hline
      MRR over 134 topics & 0.05 & 0.09 & \textcolor{blue}{+80\%} \\ %\hline
    \end{tabular}
  \end{table}

\textcolor{blue}{To favor searches over the most descriptive fields seems promising.}
 
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Problems}
\subsection{Monolingual task}

\begin{frame}
  \frametitle{Problems in the Monolingual task}
 
  \begin{description}
  \item[Initial idea] Use language identification both for topics 
and documents and build one index per language. 
 
  \item[Problem] Language identification was very noisy.

  \item[Solution] No language identification, build a unique index and launch queries.
  \end{description}

  \begin{center}
  
  The index became a monster and our system was too slow. 

We didn't have time to tame Lucene.

  For the same reasons, we couldn't try the Multilingual task. 

  \end{center}

\end{frame}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\subsection{Multilingual task}

%\begin{frame}
%  \frametitle{Problems in the Multilingual task}
 
%  \begin{description}
%  \item[Initial idea] Try to identify target language by matching key word 
% (Dutch Minister)sust language identification, anguage. Translate the topics
%    by mining Answers.com.
%  \item[Problem] Language identification was very noisy.
%  \item[Solution] No language identification, use a unique index.
%  \end{description}

%  \begin{center}
%    Same problem! Our system was too slow.
%  \end{center}

%\end{frame}




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Bilingual EN-ES task}

\begin{frame}
  \frametitle{Problems in the Bilingual EN-ES task}
 
  Not enough time to implement and test most of our initial ideas,
  such as:

  \begin{itemize}
  \item Use text anchors pointing to a page as the page's descriptors.
  \item Evaluate the impact of our OOV translation process.
  \item Improve the OOV translation process, refining the way we
    select the translation among all candidates.
  \end{itemize}
  
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Lessons}

\begin{frame}
  \frametitle{Lessons we've learned}
 
  \begin{itemize}
  \item Don't sign up in too many CLEF tracks.
  \item Dealing with huge amount of data is not straightforward.
  \item Don't trust blindly language identification.
  \item Be careful with the translations: 1/3 of the Spanish topics
    were incorrectly translated into English.
  \end{itemize}
  
\end{frame}


% \section{References}

% \begin{frame}
%   \frametitle{References}
%   \begin{itemize}
%   \item Answers.com: \texttt{http://www.answers.com}
%   \item Lucene: \texttt{http://lucene.apache.org}
%   \item S. Vogel, Y. Zhang, F. Huang. Mining translations of OOV terms
%     from the Web through Cross-Lingual Query Expansion.
%     \textit{Proceeding of SIGIR 2005}. 2005.
%   \end{itemize}
  
% \end{frame}


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\section{}

\begin{frame}
  \frametitle{The slide you were waiting for...}
    \begin{center} 
    Questions? 
    
    \vspace{1cm}
   
    Thank you! 
    \end{center} 
  
\end{frame}

\end{document}

