Software release: TiMBL 1.0

[Jakub.Zavrel@kub.nl]

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  Software release:      TiMBL 1.0
                         Tilburg Memory Based Learner
                         ILK Research Group, http://ilk.kub.nl/
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 The ILK (Induction of Linguistic Knowledge) Research Group at Tilburg
 University, The Netherlands, announces the release of TiMBL, Tilburg
 Memory Based Learner (version 1.0).

 TiMBL is a machine learning program implementing a family of
 Memory-Based Learning techniques for discrete data. TiMBL stores a
 representation of the training set explicitly in memory (hence
 `Memory Based'), and classifies new cases by extrapolating from the
 most similar stored cases.  

 TiMBL features the following (optional) metrics and speed-up
 optimalizations that enhance the underlying k-nearest neighbour 
 classifier engine:

 - Information Gain weighting for dealing with features of differing
   importance (the IB1-IG learning algorithm).
 - Stanfill & Waltz's / Cost & Salzberg's (Modified) Value Difference
   metric for making graded guesses of the match between two
   different symbolic values.
 - Conversion of the flat instance memory into a decision tree, 
   and inverted indexing of the instance memory, both yielding
   faster classification.
 - Further compression and pruning of the decision tree, guided 
   by feature information gain differences, for an even larger 
   speed-up (the IGTREE learning algorithm).

 TiMBL accepts commandline arguments by which these metrics and
 optimalizations can be selected and combined. TiMBL can read the 
 C4.5 and WEKA's ARFF data file formats as well as column files and
 compact (fixed-width delimiter-less) data.


 -[download]-----------------------------------------------------------

 You are invited to download the TiMBL package for educational or
 non-commercial research purposes. When downloading the package you
 are asked to register, and express your agreement with the license
 terms. TiMBL is *not* shareware or public domain software.

 The TiMBL software package can be downloaded from

                   http://ilk.kub.nl/software.html

 or by following the `Software' link under the ILK home page at
 http://ilk.kub.nl/ .

  The TiMBL package contains the following:

 - Source code (C++) with a Makefile.
 - A reference guide containing descriptions of the incorporated
   algorithms, detailed descriptions of the commandline options,
   and a brief hands-on tuturial.
 - Some example datasets.
 - The text of the licence agreement.
 - A postscript version of the paper that describes IGTREE.

  The package should be easy to install on most UNIX systems.

 -[background]---------------------------------------------------------

 Memory-based learning (MBL) has proven to be quite successful in a 
 large number of tasks in Natural Language Processing (NLP) -- MBL of
 NLP tasks (text-to-speech, part-of-speech tagging, chunking, light
 parsing) is the main theme of research of the ILK group. At one
 point it was decided to build a well-coded and generic tool that 
 would combine the group's algorithms, favorite optimization tricks, 
 and interface desiderata, the whole of which is now version 1.0 of
 TiMBL.

 We think TiMBL can make a useful tool for NLP research, and, for that
 matter, for any other domain with discrete classification tasks.

 For information on the ILK Research Group, visit our site at
 
                   http://ilk.kub.nl/

 On this site you can find links to (postscript versions of)
 publications relating to the algorithms incorporated in TiMBL and on
 their application to NLP tasks.

 The reference guide ("TiMBL: Tilburg Memory-Based Learner, version
 1.0, Reference Guide.", Walter Daelemans, Jakub Zavrel, Ko van der
 Sloot, and Antal van den Bosch. ILK Technical Report 98-03) can be
 downloaded separately and directly from

                   http://ilk.kub.nl/~ilk/papers/ilk9803.ps.gz

 For comments and bugreports relating to TiMBL, please send mail to

                   Timbl at kub.nl

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