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<H1>segment</H1>
<H2> NAME </H2>
segment - segment text using N-gram language model
<H2> SYNOPSIS </H2>
<PRE>
<B>segment</B> [ <B>-help</B> ] <I>option</I> ...
</PRE>
<H2> DESCRIPTION </H2>
<B> segment </B>
infers a most likely segmentation (location of segment boundaries)
from a text, based on a segment language model.
The language model is a standard backoff N-gram model in ARPA
<A HREF="ngram-format.5.html">ngram-format(5)</A>,
modeling segmentation using the boundary tags &lt;s&gt; and &lt;/s&gt;.
The program reads in a word sequence, finds the most likely locations
of segment boundaries according to the language model, and
outputs the word sequence with segment boundaries marked by &lt;s&gt; tags.
<H2> OPTIONS </H2>
<P>
Each filename argument can be an ASCII file, or a
compressed file (name ending in .Z or .gz), or ``-'' to indicate
stdin/stdout.
<DL>
<DT><B> -help </B>
<DD>
Print option summary.
<DT><B> -version </B>
<DD>
Print version information.
<DT><B>-order</B><I> n</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Set the maximal N-gram order to be used, by default 3.
NOTE: The order of the model is not set automatically when a model
file is read, so the same file can be used at various orders.
<DT><B>-debug</B><I> level</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Set the debugging output level (0 means no debugging output).
Debugging messages are sent to stderr.
<DT><B>-lm</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Read the N-gram model from
<I>file</I>.<I></I><I></I><I></I>
<DT><B>-text</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Find the text to be segmented in
<I>file</I>.<I></I><I></I><I></I>
Default input is stdin.
<DT><B> -continuous </B>
<DD>
Process all words in the input as one sequence of words, irrespective of
line breaks.
Normally each line is processed separately as a word sequence.
<DT><B> -posteriors </B>
<DD>
Use a forward-backward algorithm to compute the posterior probabilities
of a segment boundary at each word transition, and hypothesize a boundary
whenever the probability exceeds 0.5.
By default a Viterbi algorithm is used that computes
the globally most likely segmentation.
<BR>
If
<B> -continuous </B>
is specified as well,
then this option will produce one line of output per word, containing,
respectively, the &lt;s&gt; tag (if appropriate), the word itself, and the
posterior probability for a boundary preceding the word.
<DT><B> -unk </B>
<DD>
Output the unknown word token &lt;unk&gt; for each input word not in the
language model vocabulary.
The default is to output the input word unchanged.
<DT><B>-stag</B><I> string</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Use
<I> string </I>
to mark segment boundaries in the output.
Default is the start-of-sentence symbol defined in the language model (&lt;s&gt;).
<DT><B>-bias</B><I> b</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Make a segment boundary a priori more likely by a factor of
<I>b</I>.<I></I><I></I><I></I>
This allows balancing of false detection/rejection errors.
The default is 1.
</DD>
</DL>
<H2> SEE ALSO </H2>
<A HREF="ngram-count.1.html">ngram-count(1)</A>, <A HREF="ngram-format.5.html">ngram-format(5)</A>.
<BR>
A. Stolcke and E. Shriberg, ``Automatic Linguistic Segmentation of
Spontaneous Speech,'' <I>Proc. ICSLP</I>, 1005-1008, 1996.
<H2> BUGS </H2>
Only N-grams models up to trigram order are used accurately.
For higher-order models use the more general
<A HREF="hidden-ngram.1.html">hidden-ngram(1)</A>.
Andreas Stolcke &lt;stolcke@icsi.berkeley.edu&gt;
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Copyright (c) 1997-2004 SRI International
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