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\title{Statistical Natural Languege Tagging}
\date{}

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\emph{Statistical Natural Language Tagging} \cite{Charniak93}
consists of assigning a lexical category to each word in a text.
Usually, the first step in the parsing process is to identify which
lexical category (noun, verb, etc) each word in the sentence belongs to, i.e to
tag the sentence. The difficulty of the tagging process comes from the lexical
ambiguity of the words: many words can be in more that one lexical class.
The assignment of a tag to a word depends on the assignments to the other
words. Let us considered the following words and their tags.  
\begin{center}
   \begin{tabular}{ll}
   {\bf Rice}:  &NOUN\\
   {\bf flies}: &NOUN, VERB\\
   {\bf like}:  &PREP, VERB\\
   {\bf sand}:  &NOUN
   \end{tabular}
\end{center}        
The choice for the word {\em like} depends on the choice for the word {\em flies}
and vice versa.
The syntactic structure of the sentence depends on its tagging,
and this structure is fundamental to
determine its meaning.

The most common statistical models for tagging are based on
assigning a probability to a given tag according to
its neighboring tags ({\em context}). Then the tagger tries to maximize
the total probability of the tagging of each sentence.
The raw data to obtain this probability are extracted from the training text.
That is, we need a hand-tagged text such as the one provided in \cite{Brown_corpus}.
Then, the probability of a sentence can be define as the sum of the probabilities
of the context of each word $w_i$:
($\sum_i (f(w_i))$). 
The probability of a context can be defined as
\[
  f(w) = \log P(T | LC, RC)
\]
where $P(T | LC, RC)$ is the probability that the tag of word w is $T$, given that
its context is formed by the sequence of tags $LC$ to the left and the sequence $RC$ to
the right. This probability is estimated from the training text as
\[
   P(T | LC, RC) \approx \frac{occ(LC, T, RC)}{\sum_{T' \in {\cal T}} occ(LC, T', RC)}
\]                 
where $occ(LC, T, RC)$ is the number of occurrences of the list of tags $LC,T,RC$
in the training table and ${\cal T}$ is the set of all possible tags
of $w_i$. 

\begin{thebibliography}{10}  

\bibitem{Araujo02b}
L.~Araujo.
\newblock Part-of-speech tagging with evolutionary algorithms.
\newblock In {\em Proc. of the Int. Conf. on Intelligent Text Processing and
  Computational Linguistics (CICLing-2002), Lecture Notes in Computer Science
  2276}, pages 230--239. Springer-Verlag, 2002.     

\bibitem{Brill95}
E.~Brill.
\newblock Transformation-based error-driven learning and natural language
  processing: A case study in part of speech tagging.
\newblock {\em Computational Linguistics}, 21(4), 1995.    

\bibitem{Brown79}
E.~Brill.
W.N.~Francis and H.~Kucera.
\newblock Brown Corpus of Standard American English.
\newblock {\em Brown University, Providence, RI}, possible access online at the 
Linguistic Data Consortium LDC site.    

\bibitem{Charniak93}
E.~Charniak.
\newblock {\em Statistical Language Learning}.
\newblock MIT press, 1993.           

\end{thebibliography}   

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