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\title{{\bf CSXXX: NLP/Text Mining for Security}\\
       Homework Assignment \#5:\\
      Spam email classification  }

\author{}

\date{Out: Month date, Year\\
      Due: Month date, Year\\
      (xxxxx needs to be turned-in)}
\maketitle

\section{Project Deadlines}

Project proposal due: October 15, 2010
Project Checkpoint: November 29, 2010
Project Final Report due: December 9, 2010
\section{Overview}

Projects should propose and tackle a problem broadly related to NLP, whether a core NLP task or an interesting application. We expect that you will implement statistical techniques as part of the project (from the literature or novel), apply them to real-world data sets, and perform comprehensive evaluation of the techniques. Project writeups should also discuss related work/approaches in the research literature. 

You may work in groups of up to 3 people; we expect that larger groups will tackle more substantial projects.

\section{Proposal (1 or 2 pages)}

The proposal should clearly outline each of the following components: - A description of the algorithms and approaches you plan to implement - An overall plan for experiments, i.e., what experimental conditions you plan to evaluate on - Real world data-set(s) that will be used for evaluation - A discussion of related work or approaches, with references to the research literature 

\section{Checkpoint }
(up to 4 pages for one person groups, 6 for two, 8 for
three, not including references)

The checkpoint should be a substantial draft of the final writeup. We expect that the dataset should be ready and preliminary experiments conducted by this point. 

\section{Final writeup}
 (up to 4 pages for one person groups, 6 for two, 8 for
three, not including references)

The final report should be similar in quality to a research conference publication, and should provide a comprehensive overview of the project, covering all the points discussed in the Overview. 

\section{Presentation session}

The last two class sessions will be devoted to presenting the projects. Each
group will present your work, and all of you will have a chance to learn about
each others' work. The quality of the slides and presentation will be part of
the overall project grade.

\section{Project Ideas}

Here are some sample project ideas.
\begin{enumerate}
  \item PHRASE EXTRACTION
  
We currently have a massive set (100,000+) of consumer reviews of restaurants
scraped from citysearch.com (collected by the Spoken Language Systems group
here at MIT). A major step towards making this information more useful is to
produce short keyphrase summaries of multiple sets of reviews. We currently
have a small set of phrases that were manually extracted from a subset of the
reviews. Can you build a more robust keyphrase extraction system, preferably
one that aggregates related keyphrases together? What features may be
applicable here?

\item CROSS-LINGUAL NAMED ENTITY RECOGNITION

A named entity is a short string of words that identifies a person,
organization, location, etc. Roughly speaking, named entities correspond to
proper nouns in English. Automatically recognizing named entities in English
is relatively easier because proper nouns are capitalized. In many languages
such orthographic cues are not readily available, making named entity
recognition more difficult. However, it is frequently possible to obtain
alignments between parallel texts in different languages. Is it possible to
leverage this alignment and transfer capitalization information across
languages to improve named entity recognition in the other language?

\item PREDICTING LANGUAGE HISTORY

Many documents, such as United Nations proceedings, have been translated into
hundreds of different languages. These languages historically form a
genealogical tree where closely related languages will tend to have
lower-height common ancestors. Would it be possible to use statistical
properties of the parallel documents to reconstruct a language tree? What
features would be most useful for doing so?
\end{enumerate}
\end{document}
