:: libTextCat 2.2 ::
     
  What is it?
  
     Libtextcat is a library with functions that implement the
     classification technique described in Cavnar & Trenkle, "N-Gram-Based
     Text Categorization" [1]. It was primarily developed for language
     guessing, a task on which it is known to perform with near-perfect
     accuracy.
     
     The central idea of the Cavnar & Trenkle technique is to calculate a
     "fingerprint" of a document with an unknown category, and compare this
     with the fingerprints of a number of documents of which the categories
     are known. The categories of the closest matches are output as the
     classification. A fingerprint is a list of the most frequent n-grams
     occurring in a document, ordered by frequency. Fingerprints are
     compared with a simple out-of-place metric. See the article for more
     details.
     
     Considerable effort went into making this implementation fast and
     efficient. The language guesser processes over 100 documents/second on
     a simple PC, which makes it practical for many uses. It was developed
     for use in our webcrawler and search engine software, in which it it
     handles millions of documents a day.
     
  Download
  
     The library is released under the BSD License, which basicly states
     that you can do anything you like with it as long as you mention us
     and make it clear that this library is covered by the BSD License. It
     also exempts us from any liability, should this library eat your hard
     disc, kill your cat or classify your attorney's e-mails as spam.
     
     The current version is 2.1.

     It can be downloaded from our website: 

       http://software.wise-guys.nl/libtextcat/ 

     As of yet there is no development version.
     
  Installation
  
     Do the familiar dance:

       tar xzf libtextcat-2.2.tar.gz
       cd libtextcat-2.2
       ./configure
       make
       make install

     This will install the library in /usr/local/lib/ and the createfp
     binary in /usr/local/bin.
     
     The library is known to compile flawlessly on GNU/Linux for x86, and
     IRIX64 (both 32 and 64 bits). 
     
  Quickstart: language guesser
  
     Assuming that you have successfully compiled the library, you still
     need some language models to start guessing languages. If you don't
     feel like creating them yourself (cf. [2]Creating your own
     fingerprints below), you can use the excellent collection of over 70
     language models provided in Gertjan van Noord's "TextCat" package. 
     You can find these models and a matching configuration file 
     in the langclass directory:

       * cd libtextcat-2.2/langclass/
       * ../src/testtextcat conf.txt
  	 
     Paste some text onto the commandline, and watch it get classified.
     
  Using the API
  
     Classifying the language of a textbuffer can be as easy as:

       #include "textcat.h"
       ...
       void *h = textcat_Init( "conf.txt" );
       ...
       printf( "Language: %s\n", textcat_Classify(h, buffer, 400);
       ...
       textcat_Done(h);
      
  Creating your own fingerprints
  
     The createfp program allows you to easily create your own document
     fingerprints. Just feed it an example document on standard input, and
     store the standard output:

       % createfp < mydocument.txt > myfingerprint.txt
  
     Put the names of your fingerprints in a configuration file, add some
     id's and you're ready to classify.
     
  Performance tuning
  
     This library was made with efficiency in mind. There are couple of
     parameters you may wish to tweak if you intend to use it for other
     tasks than language guessing.
     
     The most important thing is buffer size. For reliable language
     guessing the classifier only needs a couple of hundreds of bytes max.
     So don't feed it 100KB of text unless you are creating a fingerprint.
     
     If you insist on feeding the classifier lots of text, try fiddling
     with TABLEPOW, which determines the size of the hash table that is
     used to store the n-grams. Making it too small will result in many
     hashtable clashes, making it too large will cause wild memory
     behaviour and both are bad for the performance.
     
     Putting the most probable models at the top of the list in your config
     file improves performance, because this will raise the threshold for
     likely candidates more quickly.
     
     Since the speed of the classifier is roughly linear with respect to
     the number of models, you should consider how many models you really
     need. In case of language guessing: do you really want to recognize
     every language ever invented?
     
  Acknowledgements

     The language models are copyright Gertjan van Noord.

  References
  
     [1] The document that started it all can be downloaded at John M.
     Trenkle's site: N-Gram-Based Text Categorization
     
       http://www.novodynamics.com/trenkle/papers/sdair-94-bc.ps.gz
  
     [2] The Perl implementation by Gertjan van Noord (code + language
     models): downloadable from his [7]website
  
       http://odur.let.rug.nl/~vannoord/TextCat/
     
  Contact
  
     Praise and flames may be directed at us through
     [email protected]. If there is enough interest, we'll whip up
     a mailing list. The current project maintainer is Frank Scheelen.
       
  c. 2003 WiseGuys Internet B.V.