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\documentclass[dvips]{beamer}
\usepackage{pstricks,pst-node,pst-tree,color,colortbl}

\usetheme{Berlin}
\beamertemplatenavigationsymbolsempty


\title{The HAIT feeling}
\author{Hans Paijmans}
\institute{
Human Aspects of Information Technology (HAIT)\\
\texttt{paai@uvt.nl}\\
\texttt{http://ilk.uvt.nl}
}
\date{
}

\begin{document}
\definecolor{MyDarkBlue}{rgb}{0,0.74902,0.9} 

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\begin{frame}
\vspace{-1pt}
\includegraphics[width=\textwidth]{hait_intro}
\end{frame}
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\begin{frame}
\begin{block}{Human Aspects of Information Technology (HAIT) 1/24}

\begin{itemize}
\item Website description
  \begin{itemize}
  \item Specialist in language and speech technology
  \item Developer of IT-services
  \item Academic researcher\ldots
  \end{itemize}
\item <2-> In practice (imagine a birtday party)
  \begin{itemize}
  \item <2->[them] \ldots and what do you do\ldots?
  \item <3->[you] I'm a language and speech technology specialist\ldots
  \item <4->[them] A \emph{what?}\ldots
  \item <5->[you] A language and speech technology specialist
  \item <5->[you] I try to make the computer understand language\ldots
  \item <6->[them] Ah, computer languages\ldots
  \item <7->[you] No, natural language, you know, what we speak\ldots
  \item <8->[them] That is so \emph{cool}!
  \end{itemize}
\end{itemize}
\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Contents 2/24}
To give you an idea what you will find in Tilburg, we will show you three examples of research and teaching in HAIT

\begin{itemize}
\item Searching the Internet: e.g. Google
\item Language and meaning: Chronological expressions
\item Language and numbers: Zipf

\end{itemize}
\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 1: searching the web 3/24}
The first example shows the basics of (internet) search engines.\\
\begin{center}
\includegraphics[width=6cm]{google}
\end{center}
\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 1: searching the Web 4/24}
To search the web, you need a search engine. But what is a search engine?\\
Answer:
\begin{itemize}
\item A search engine is a system that, given a search query (generally  keywords), provides:
  \begin{itemize}
  \item a set or list of documents
  \item that are relevant to the query.
  \end{itemize}

\item How do they work...
   \begin{itemize}
   \item <2-> Build an index (preferably weighted)
   \item <2-> Compare documents and queries
   \end{itemize}
\end{itemize}

\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 1: searching the Web 5/25}
Building - and weighting - the index:

\begin{itemize}

\item Words are not created equal
  \begin{itemize}
  \item Consider the words 'the', 'dog' and 'IBM'
  \item What are the differences...?
  \end{itemize}

\item <2-> Frequency-based criteria, for example...:
\item <2-> tf.idf...
\item <2-> ...where the weight increases with the frequency in the document but decreases with the document frequency (think about that for a moment)...

\item <3-> 'the'and 'ibm' have both a frequency of 10 in document A.
\item <3-> But 'the' occurs in \emph{all} of the 100 documents and 'ibm' in only two.
\item <3-> The tf.idf of 'and' is $10/100$; that of 'ibm' is $10/2$.
\end{itemize}
\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 1: searching the Web  6/25}
Comparing the documents (and queries)
\begin{itemize}
\item Pages are not created equal either
\item However, many pages may match a query...
\item <2->Which pages are best?  How to rank them?

  \begin{itemize}
  \item As we have seen, keywords may have different weights
  \item Text in titles/headings is important
  \item Text earlier in the page is important
  \item Text of links \emph{to this page} is important
  \item Important pages link to \emph{other} important pages
  \end{itemize}
\end{itemize}

\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 1: searching the Web 7/24}
A good model to compare documents is the Vector Space Model.
\begin{itemize}
\item The next slide shows a very simple vector space with only three keywords.\\
We also have drawn in three documents (the red lines)...
\item <2->...the words have binary weights \\(equal distances from the origin)...
\item <3->...and there are no semantic differences \\(the axes stand under an angle
  of 90 degrees).

\end{itemize} 

\end{block}
\end{frame}
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\begin{frame}
\begin{block}{Example 1: searching the Web 8/24}

\includegraphics[width=9cm]{vector}
\end{block}

\end{frame}

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\begin{frame}
\begin{block}{Example 2: Chronological Expressions 9/24}
The next example shows an application of Memory Based learning.\\
\begin{center}
\includegraphics[width=7cm]{Ottoman}
\end{center}
\end{block}
\end{frame}
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\begin{frame}
\begin{block}{Example 2: Chronological Expressions 10/24}
Let us return to Google: imagine you want to find pages about Turkey in the time of the Ottoman empire.

\begin{itemize}
  \item You type ``Turkey ottoman''...
  \item and you get all pages with these words. BUT...

  \item <2->...if a page is about Turkey between 1500 and
    1950 and the word 'ottoman' does not occur on that page... 

  \item <3->... you will not find that page.

  \end{itemize}
\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 2: Chronological Expressions 11/24}
The Ottoman empire existed for 550 years. If you want to be certain to find
all pages that combine 'turkey' with any or all of those years, you have to
type all queries:

\begin{itemize}
  \item ``turkey 1500', 'turkey 1501', 'turkey 1502' etc...''

  \item <2->... which is absurd...


  \item <3->... and you will nevertheless miss 'seventeenth century,
    17th century, XVIIth century and all such terms...


  \item <4->... and you will find a {\em lot} of telephone numbers, prices and
    inventory numbers!

  \end{itemize}
\end{block}
\end{frame}
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\begin{frame}
\begin{block}{Example 2: Chronological Expressions 12/24}

The solution here may be found in Memory based learning (MBL). \\
In Memory Based Learning, you first
\begin{itemize}
\item collect a large database with descriptions of examples,
\item and assign to each example the correct class...
\item <2-> New cases are compared with the examples in the database
\item <2-> and given the class of the examples that are most similar...
 
\item <3-> Of course this is not so easy as it looks...
\begin{itemize}
\item <4-> Which features are the important ones?
\item <4-> How do you weigh them?
\item <4-> How do you measure the differences between the examples...?
\end{itemize}

\item <5-> Actually, it does not differ so much from the first example about search engines.
\end{itemize}
\end{block}
\end{frame}


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\begin{frame}
\begin{block}{Example 2: Chronological Expressions 13/24}
Machine Based Learning: scheme
\includegraphics[width=9cm]{mbl-cycle}
\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 2: Chronological Expressions 14/24}

\begin{table}
\begin{tiny}
\begin{center}
\begin{tabular}{|l l l l|l|l l l l|>{\columncolor[gray]{.8}}l|}

\rowcolor[gray]{0.8} Flat & roof & tile & 1 & \textcolor{red}{0.193} & Post & - & roman & 34 & \textcolor{MyDarkBlue}{[other]}\\
\rowcolor[gray]{0.8}Depth & : & l & ; & \textcolor{red}{0.2} & m & 18.8 & m & OD &  \textcolor{MyDarkBlue}{[other]}\\
\rowcolor[rgb]{0,0.74902,1}is & first & seen & in & \textcolor{red}{675} & AD & when & it & was & \textcolor{yellow}{[Timespan]}\\
\rowcolor[rgb]{0,0.74902,1}Pitcher & handle & 22 & 0.443 & \textcolor{red}{10th} & to & 13th & cent & 30 & \textcolor{yellow}{[Timespan]}\\
\rowcolor[gray]{0.8}75 & human & bone & - & \textcolor{red}{0.095} & 76 & Pottery & 1 & 0.045 &  \textcolor{MyDarkBlue}{[other]}\\
\rowcolor[gray]{0.8}deposit & was & up & to & \textcolor{red}{0.1} & m & thick & and & sloped &  \textcolor{MyDarkBlue}{[other]}\\
\rowcolor[rgb]{0,0.74902,1}occurs & in & charters & from & \textcolor{red}{1128} & and & 1153 & . & The & \textcolor{yellow}{[Timespan]}\\

\end{tabular}
\end{center}
\end{tiny}

\end{table}

The class of the focusword (red) is defined by its context.

\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 2: Chronological expressions 15/24}
In this way we can index every expression in a document that indicates a year
or an era.\\
Similar techniques are used to disambiguate geographical names or to extract names of persons and institutions.\\
Outside IR there are even more important linguistic applications for Memory Based Learning:
\begin{itemize}
\item Part of speech tagging
\item morphological segmentation
\item machine translation
\item and so on.
\end{itemize}



\end{block}
\end{frame}
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\begin{frame}
\begin{block}{Example 3: Zipf... 16/14}
The last example shows just wat you can do when you count words.\\
\begin{center}
%% \includegraphics[width=7cm]{turks}
\end{center}
\end{block}
\end{frame}
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\begin{frame}
\begin{block}{Example 3: Zipf... 17/14}

Let us look at a typical frequency list of words in a text.\\

\begin{table}
\begin{tiny}
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
rank & freq. & word & freq. & word\\
     &       & sorted & sorted &  \\
\hline
1 & 99  & A      & 2387 & the\\
2 & 1   & ACK    & 1381 & of\\
3 & 3   & AD     &  814 & in\\
4 & 34  & ALG    & 787 & and\\
5 & 4   & ANSI   & 776 & a\\
6 & 2   & ARFF   & 770 & to\\
7 & 1   & ARPAnet & 622 & is\\
8 & 1   & AS     & 618 & small\\
9 & 29  & ASCII  & 467 & that\\
10 & 1  & AWK    & 443 & The\\
11 & 1  & Abort  & 437 & tt\\
... & ... & ... & ... & ... \\
4547 & 18 & you & 1 & Abuse\\
4548 & 11 & your &   1 & Absicht\\
4549 & 14 & z & 1 & About\\
4550 & 2 & zaag &   1 & Aborts\\
4551 & 1 & zealand & 1 & Abort\\
\hline
\end{tabular}
\end{center}
\end{tiny}

\end{table}

The next slide shows graphs of frequency against rank.
\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 3: Zipf... 18/24}
\begin{tabular}{ c c}
\includegraphics[width=5cm]{z1}
\includegraphics[width=5cm]{z2}
\end{tabular}
An important insight was discovered before the war, when Zipf discovered the relation between rank and frequency of tokens in a language and formulated his 'law of least effort', not unlike Pareto's distribution.\\
\end{block}
\end{frame}




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\begin{frame}
\begin{block}{Example 3: Zipf... 19/24}

Let us have a closer look at the possible relations between words and meaning in terms of speaker/listener effort:
 
\begin{tiny}
\begin{center}
\begin{tabular}{|l|l|l|l||l|l|l|}
\hline
         & word1 & word2 & word3 & word1 & word2 & word3\\
\hline
meaning1 & 1     & 0     & 0     & 1     & 0     & 0 \\
meaning2 & 1     & 0     & 0     & 0     & 1     & 0 \\
meaning3 & 1     & 0     & 0     & 0     & 0     & 1 \\
\hline
& \multicolumn{3}{|c|} {No effort for speaker}  & \multicolumn{3}{|c|}{No effort for listener}\\

\hline
\end{tabular}
\end{center}
\end{tiny}

In 2002 two spanish scientists started from this matrix and
computed the invested effort for speaker and listener for all word-meaning combinations.\\

The next slide shows some very interesting graphs:
\end{block}
\end{frame}


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\begin{frame}
\begin{block}{Example 3: Zipf... 20/24}
\begin{center}
\includegraphics[width=8cm]{ferrer-effort-graphs}\\
\end{center}

\begin{footnotesize}
This figure and that of the next slide are from Ferrer/Sole "Least effort and the origins of scaling in human language",2003.\\
\end{footnotesize}

\begin{itemize}
\item Horizontally, $\lambda$ shows the ratio of effort of speaker/listener\\
\item The vertical axis shows the mutual information (A) and the effective lexicon (B)
\end{itemize}

\end{block}
\end{frame}

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\begin{frame}
\begin{block}{Example 3: Zipf... 21/24}
If we translate the matrices of $\lambda < 41$, $\lambda=41$ and $\lambda>41$ into frequency--rank graphs, we see that the matrix of $\lambda=41$ displays the Zipf curve.\\ 
\includegraphics[width=9cm]{ferrer-graphs}\\

This demonstrates the unique and very narrow position of human language between all other communicative models.
\end{block}
\end{frame}


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\begin{frame}
\begin{block}{Example 3: Zipf... 22/24}
This is all very nice, but what can we do with it?
\begin{itemize}

\item <2-> To start with: it gives new insights in the development of language.
\item <2-> New and better models of human communication can profit from such insights.
\item <2-> It helps with understanding animal communication. \\
In fact, dolphins and some other animals display Zipfian distributions in their communication systems.
\item <2-> It can be used for decyphering unknown or ancient languages.
\end{itemize}

\end{block}
\end{frame}




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\begin{frame}
\begin{block}{Example 3: Zipf... 23/24}

Finally, when my students get very bored, I ask them to decypher the so-called Voynich manuscript.
\begin{center}
\includegraphics[width=5cm]{voynich-01}
\end{center}

\begin{itemize}
\item <2->It purportedly is a 16-th century manuscript in an unknown language, with pictures of unknown plants, cities and objects...
\item <3-> But it may also be a hoax, albeit a very complicated one...
\item <4-> To this moment, linguists have been unable to decide.


\end{itemize}


\end{block}
\end{frame}
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\begin{frame}
\begin{block}{Example 3: Zipf... 24/24}

\begin{center}
\includegraphics[width=7cm]{voynich-01}\\


{\bf So now you can have a go at it!}
\end{center}

\end{block}
\end{frame}



\end{document}


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