Commit adcd87f9 authored by Loïc Barrault's avatar Loïc Barrault
Browse files

textprocessing updates

parent 025ecbab
To
be
honest
found
unison
to
be
somewhat
flaky
in
particular
it
seems
to
lose
its
secret
mission
index
files
and
not
that
rarely
while (<>) {
if (/^(\w+)\s*$/) {
push @words, $1;
}
}
foreach $w (sort @words) {
print ">> $w\n";
}
#while (<>) {
# if (/\bsecret\s+mission\b/i) {
# print;
# }
#}
% VERSION DATE: March 2007
\usepackage{graphicx}
\usepackage{fancyhdr}
\usepackage{comment}
\newenvironment{mainfont}{\begin{sf}}{\end{sf}}
\newcommand{\mybf}{\bf\sf}
%\renewenvironment{mainfont}{\begin{tt}}{\end{tt}}
%\renewcommand{\mybf}{\bf\tt}
\newlength{\qupartSpacing}
\setlength{\parskip}{2mm}
\setlength{\parindent}{0mm}
\setlength{\topmargin}{-11.5mm}
\setlength{\textheight}{246mm}
\setlength{\oddsidemargin}{-5mm}
\setlength{\evensidemargin}{-5mm}
\setlength{\textwidth}{166mm}
\setlength{\qupartSpacing}{3mm}
\pagestyle{empty}
\pagestyle{fancy}
\renewcommand{\headrulewidth}{0mm}
\renewcommand{\footrulewidth}{0mm}
\cfoot{{\bf\thepage}}
\lfoot{{\bf\pressmarktext}}
\rhead{{\bf\pressmarktext}}
\lhead{\tuoslogo}
\rfoot[{\bf CONTINUED}]{{\bf TURN OVER}}
\newcommand{\examsection}[1]%
{\begin{center}{\mybf SECTION #1}\end{center}}
\newlength{\mymarklength}
\newcommand{\mymarktext}[1]{%
\settowidth{\mymarklength}{#1}%
\addtolength{\mymarklength}{-3mm}%
\rule{\mymarklength}{0mm}%
\hfill
\makebox[0mm][l]{\hspace*{-1\mymarklength}\hspace*{2mm}#1}}
\newcommand{\mymark}[1]{\mymarktext{[#1 marks]}}
\newcommand{\mymarkone}{\mymarktext{[1 mark]}}
\newcommand{\mypercent}[1]{\mymarktext{[#1\%]}}
\newcounter{questionnumber}
\newcounter{qupartnumber}[questionnumber]
\newcounter{exitemnumber}[qupartnumber]
\newcounter{answerWidth:exam}
\setcounter{answerWidth:exam}{167}
\newcounter{answerWidth:question}
\setcounter{answerWidth:question}{158}
\newcounter{answerWidth:qupart}
\setcounter{answerWidth:qupart}{148}
\newcounter{answerWidth:exlist}
\setcounter{answerWidth:exlist}{135}
\newcounter{answerWidth}
\setcounter{answerWidth}{\value{answerWidth:exam}}
\newenvironment{question}%
{\stepcounter{questionnumber}%
\setcounter{answerWidth}{\value{answerWidth:question}}%
\begin{list}{}{\setlength{\leftmargin}{6mm}
\setlength{\labelwidth}{4mm}
\setlength{\labelsep}{2mm}
\setlength{\topsep}{4mm}
\setlength{\rightmargin}{4mm}}%
\item[{\mybf\arabic{questionnumber}.}]}%
{\end{list}%
\setcounter{answerWidth}{\value{answerWidth:exam}}}
\newenvironment{qupart}%
{\stepcounter{qupartnumber}%
\setcounter{answerWidth}{\value{answerWidth:qupart}}%
\vspace*{\qupartSpacing}%
\begin{list}{}{\setlength{\leftmargin}{10mm}
\setlength{\labelwidth}{4mm}
\setlength{\labelsep}{6mm}
\setlength{\topsep}{-1mm}
\setlength{\itemsep}{0mm}
\setlength{\rightmargin}{0mm}}%
\item[{\alph{qupartnumber})}]}%
{\end{list}%
\setcounter{answerWidth}{\value{answerWidth:question}}}
\newenvironment{exlist}%
{\begin{list}{}{%
\setcounter{answerWidth}{\value{answerWidth:exlist}}%
\setlength{\leftmargin}{12mm}%
\setlength{\labelwidth}{8mm}%
\setlength{\labelsep}{4mm}%
\setlength{\topsep}{3mm}%
\setlength{\itemsep}{3mm}%
\setlength{\rightmargin}{0mm}}}%
{\end{list}%
\setcounter{answerWidth}{\value{answerWidth:qupart}}}
\newcommand{\exitem}%
{\stepcounter{exitemnumber}%
\item[{(\roman{exitemnumber})}]}
\newcommand{\tuoslogo}{%
\setlength{\unitlength}{1mm}%
\begin{picture}(0,0)%
\put(0,-25){\includegraphics[height=25mm]{tuoslogo_bw_nobo.eps}}%
\linethickness{0.1mm}%
\put(85,5){\line(-1,0){110.2}}%
\linethickness{0.4mm}%
\put(85,5){\line(0,-1){35}}%
\linethickness{0.6mm}%
\put(85,-30){\line(-1,0){110.2}}%
\end{picture}%
\renewcommand{\tuoslogo}{}}
\newenvironment{exam}%
{\begin{mainfont}
\examonly{\vspace*{35mm}}
\showTitleModelSolutions%
\showDataProvided
\examonly{\vspace*{15mm}}
\examonly{\doNotRemoveText}%
\\[5mm]
{\departmenttext \hfill \examdatetext \\[6mm]
\examtitletext \hfill \examtimetext
\\[9mm] \rubrictext}%
\resetanswersonly%
\answersonly{\lhead{{\bf UNIVERSITY OF SHEFFIELD}}}
\answersonly{\rfoot{}}
\examonly{\studentRegistrationNo}%
\examonly{\newpage\lhead{}}}%
{\rfoot{}\end{mainfont} }
\newcommand{\questionsend}%
{\begin{center}%
{\bf END OF QUESTION PAPER}%
\end{center}}
\newcommand{\questioncontinued}%
{\examonly{\vspace*{5mm}%
\hfill \hfill {\bf QUESTION CONTINUED ON THE NEXT PAGE}%
\newpage}}
\newcommand{\examtitletext}{}
\newcommand{\examtitle}[1]{\renewcommand{\examtitletext}{#1}}
\newcommand{\degreestext}{}
\newcommand{\degrees}[1]{\renewcommand{\degreestext}{#1}}
\newcommand{\departmenttext}{}
\newcommand{\department}[1]{\renewcommand{\departmenttext}{#1}}
\newcommand{\pressmarktext}{}
\newcommand{\pressmark}[1]{\renewcommand{\pressmarktext}{#1}}
\newcommand{\examdatetext}{}
\newcommand{\examdate}[1]{\renewcommand{\examdatetext}{#1}}
\newcommand{\examtimetext}{}
\newcommand{\examtime}[1]{\renewcommand{\examtimetext}{#1}}
\newcommand{\rubrictext}{}
\newcommand{\rubric}[1]{\renewcommand{\rubrictext}{#1}}
\newcommand{\universitytext}{{\bf THE UNIVERSITY OF SHEFFIELD}}
\newcommand{\university}[1]{\renewcommand{\universitytext}{#1}}
\newcommand{\showDataProvided}{{\bf Data Provided:
\dataProvidedText}}
\newcommand{\dataProvidedText}{{\bf None}}
\newcommand{\dataProvided}[1]{%
\renewcommand{\dataProvidedText}{%
{\bf ~ \\ \parbox[t]{100mm}{\bf#1}}}}
\newcommand{\setterText}{}
\newcommand{\setter}[1]{\renewcommand{\setterText}{#1}}
\newcommand{\showTitleModelSolutions}{}
\newcommand{\examonly}[1]{#1}
\newcommand{\answersonly}[1]{#1}
\newcommand{\resetanswersonly}{\renewcommand{\answersonly}[1]{}}
\excludecomment{answer}
\newcommand{\showanswers}{
\renewenvironment{answer}%
{\begin{answershown}}%
{\end{answershown}}%
\renewcommand{\examonly}[1]{}%
\renewcommand{\resetanswersonly}{}%
\renewcommand{\showTitleModelSolutions}{~\\%
\centerline{\underline{\large\bf MODEL SOLUTIONS}}%
\\[10mm]%
% {\bf SETTER(S):}~%
{\bf SETTER:}~%
\parbox[t]{100mm}{\bf\setterText}%
\\[5mm]}}
\newenvironment{answershown}%
{\vspace*{4mm}%
\makebox[0mm][l]{%
\setlength{\unitlength}{1mm}%
\linethickness{0.4mm}%
\begin{picture}(0,0)
\put(-2,4){\line(0,-1){4}}
\put(\value{answerWidth},4){\line(0,-1){4}}
\addtocounter{answerWidth}{2}
\put(-2,4){\line(1,0){\value{answerWidth}}}
\addtocounter{answerWidth}{-2}
\end{picture}}{\sc answer:}\\[2mm]}%
{~\\ \makebox[0mm][l]{%
\setlength{\unitlength}{1mm}%
\linethickness{0.3mm}%
\begin{picture}(0,0)
\put(-2,0){\line(0,1){3}}
\put(\value{answerWidth},0){\line(0,1){3}}
\addtocounter{answerWidth}{2}
\put(-2,0){\line(1,0){\value{answerWidth}}}
\addtocounter{answerWidth}{-2}
\end{picture}}\vspace*{2mm}}
\newcommand{\studentRegistrationNo}{%
\vfill
{\bf Registration number from U-Card (9 digits)
--- to be completed by student } \\[3mm]
~ \hspace*{77mm}%
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\hline
~~~~ & ~~~~ & ~~~~ & ~~~~ & ~~~~ & ~~~~
& ~~~~ & ~~~~ & ~~~~ \\[4mm] \hline
\end{tabular}
\vspace*{10mm}}
\newcommand{\doNotRemoveText}{}
\newcommand{\doNotRemove}{%
\renewcommand{\doNotRemoveText}{%
\\
{\bf PLEASE LEAVE THIS EXAM PAPER ON YOUR DESK.\\
DO NOT REMOVE IT FROM THE HALL.}\vspace*{10mm}}%
}
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\ No newline at end of file
<document docid=1>
Mary had a little lamb.
Its fleece was white as snow.
</document>
<document docid=2>
Little Miss Muffet, sat on her tuffet.
</document>
<document docid=3>
Mary, Mary, quite contrary.
How does your garden grow?
</document>
\documentclass[12pt]{book}
\usepackage{tuos_exam}
\usepackage{amsfonts}
\usepackage{amsmath}
%\showanswers % *** TOGGLE IN/OUT TO SHOW/HIDE ANSWERS ***
\pressmark{COM3110/COM6150}
%\pressmark{COM3110}
%\pressmark{COM6150}
\department{{\bf DEPARTMENT OF COMPUTER SCIENCE}}
\examtitle{{\bf TEXT PROCESSING}}
\examdate{{\bf Autumn Semester 2008-09}}
\examtime{{\bf 2 hours}}
% \dataProvided{Deduction Rules}
\setter{Mark Hepple}
\rubric{{\bf Answer THREE questions.
All questions carry equal weight. Figures in square brackets indicate the
percentage of available marks allocated to each part of a question.}}
%\doNotRemove %% MODIFIES FRONT PAGE TO "DO NOT REMOVE FROM HALL" FORMAT
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\newcommand{\stretchit}[1]{\addtolength{\itemsep}{#1mm}}
\newcommand{\argmax}{\operatornamewithlimits{argmax}}
\newcommand{\myargmax}[1]{\begin{array}[t]{c}\argmax\\{^#1}\end{array}}
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\put(0,-#2){\framebox(#1,#2){}}
\end{picture}~~\begin{minipage}[t]{#1mm}}%
{\end{minipage}}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
\begin{exam}
\begin{question}
\begin{qupart}
What is {\it stemming\/}? Briefly indicate the different kinds of
approach taken to this task. Suggest a text processing context where
stemming might be used.
\mypercent{20}
\begin{answer}
{\it Stemming\/} refers to the process of reducing words that are
morphological variants to their common root or stem, e.g. so that
variants {\it computer, computes, computed, computing}, etc, are
reduced to the stem {\it compute\/} (or some surrogate for the real
root, such as {\it comput\/}). This is a form of {\it term
conflation}. Approaches to this task range from linguistically-based
lemmatizers to fast/simple suffix-stripping methods. Lemmatizers
embody significant linguistic knowledge, and reduce words to their
linguistically-correct {\it root\/} form, using lexical look-up to
deal with irregular forms (e.g. {\it goose/geese\/}), and otherwise
using morphological rules to `undo' processes of morphological
derivation. Simple stemmers encode little or no linguistic knowledge,
do not use lexical look-up (allowing them to be much faster), and use
simple string-matching rules to strip (or sometimes substitute)
suffixes. Their output forms are often not real roots. A well-known
example of a simple stemmer is the {\it Porter stemmer}.
One text processing context where stemming may be used is IR, where
its use can allow that a query containing one morphological variant of
a word can retrieve documents not containing that term, but rather
other variants of the same stem (e.g. a query with {\it computing\/}
retrieving a document containing {\it computer\/}). The usefulness of
this move, however, is debated. Stemming will also produce some
reduction in the size of document indexes.
Other cases where stemming might be used include many applications
where document similarity is computed, e.g. document clustering, or
where similarity is computed at other levels, e.g. for sentences, in a
sentence-extraction approach to summarisation.
\end{answer}
\end{qupart}
\answersonly{\newpage}
\begin{qupart}
What is {\it part-of-speech-tagging\/}? Explain why this task is difficult,
i.e. why it requires something more than just simple dictionary
look-up.
\mypercent{20}
\begin{answer}
This is the task of assiging to each word (or more generally token) in
texts, a tag or label for the word's part of speech class, such as
noun, verb or adjective. These classes group together words that
exhibit similar distributional behaviour, i.e. which play similar
roles in the syntax of the language. The task is non-trivial due to
(i)~ambiguity, i.e. many words have more than one POS, and the correct
one must be selected (on the basis of local context), and because (ii)
it is common to encounter unknown words (i.e. ones not present in the
dictionary, even if this has been compiled from a large amount of
text), for which a POS tag must be `guessed' (using context and
morphological information).
\end{answer}
\end{qupart}
\begin{qupart}
What is the noisy channel model? Give a diagram of the model as part
of your answer. Suggest a text processing context where the noisy
channel model has been used.
\mypercent{20}
\begin{answer}
Using information theory, Shannon modeled the goal of communicating
down a telephone line -- or in general across any channel -- in the
following way. Imagine communication along a low-quality telephone
line where the sender creates a message which is being transmitted
over a channel with limited capacity; the receiver has to guess what
the original message was. It is assumed that the output of the channel
depends probabilistically on the input. The goal is to encode the
message in such a way that it occupies minimal space while still
containing enough redundancy to be able to detect and correct
errors. On receipt, the message is decoded to give what was most
likely the original message.
\[
\stackrel{W}{\longrightarrow}\fbox{Encoder}
\stackrel{X}{\longrightarrow}\fbox{\parbox{1in}{Noisy
Channel \center{$p(y|x)$}}}
\stackrel{Y}{\longrightarrow}\fbox{Decoder}
\stackrel{\hat{W}}{\longrightarrow}
\]
A simplified version of the noisy channel model has been used to model
the machine translation process. As an example, suppose we want to
translate a text from English to French. The channel model for
translation assumes that the true text is French, but that
unfortunately when it was transmitted to us, it went through a noisy
communication channel and came out as English. All we need to do in
order to translate is to recover the original French, i.e., to decode
the English to get the French.
\end{answer}
\end{qupart}
\answersonly{\newpage}
\begin{qupart}
Write a short program, in python or perl, which reads text from a file
and prints out any lines of the file that contain, within a single
line, a URL (`web address') expression. Assume the following
requirements for identifying a URL string:
\begin{exlist}
\item[i.] the string should fall between double quotes ({\tt "})
\vspace*{-2mm}
\item[ii.] it should start with {\tt http://}
\vspace*{-2mm}
\item[iii.] it should end with {\tt.htm} or {\tt.html}
\vspace*{-2mm}
\item[iv.] between this start and end, there may be any sequence of
characters except that {\tt "}, {\tt <} and {\tt >} may not appear.
\end{exlist}
\vspace*{-5mm}
\mypercent{20}
\begin{answer}
A solution in Python:
\medskip
\hspace*{20mm}
\begin{myfbox}{100}{35}
\begin{small}
\begin{verbatim}
import sys, re
f = open(sys.argv[1],'r')
urlRE = re.compile(r'"http://[^"<>]+.html?"')
for line in f:
if urlRE.search(line):
print line,
f.close()
\end{verbatim}
\end{small}
\end{myfbox}
\end{answer}
\end{qupart}
\begin{qupart}
Write a short program, in python or perl, that reads a file containing
an unsorted list of words plus count values (one word and count value
per line, separated by whitespace). The program should then print out
the words in {\it descending order\/} of their associated count
values. Partial credit (up to 15\%) will be given for an answer
that instead prints the words in {\it alphabetically sorted order}.
\mypercent{20}
\begin{answer}
A solution in Python:
\medskip
\hspace*{20mm}
\begin{myfbox}{100}{59}
\begin{small}
\begin{verbatim}
import sys
f = open(sys.argv[1],'r')
counts={}
for line in f:
toks = line.split()
counts[toks[0]]=int(toks[1])