Memory-Based Learning (MBL) is a machine-learning method applicable to a wide
range of tasks in Natural Language Processing (NLP).
The Tilburg Memory Based Learner, TiMBL, is a tool for NLP research, and for
many other domains where classification tasks are learned from examples. It
is an efficient implementation of k-nearest neighbor classifier.
TiMBL's features are:
* Fast, decision-tree-based implementation of k-nearest neighbor
* Implementations of IB1 and IB2, IGTree, TRIBL, and TRIBL2 algorithms;
* Similarity metrics: Overlap, MVDM, Jeffrey Divergence, Dot product, Cosine;
* Feature weighting metrics: information gain, gain ratio, chi squared,
* Distance weighting metrics: inverse, inverse linear, exponential decay;
* Extensive verbosity options to inspect nearest neighbor sets;
* Server functionality and extensive API;
* Fast leave-one-out testing and internal cross-validation;
* and Handles user-defined example weighting.
TiMBL is a product of the Centre of Language and Speech Technology
(Radboud University, Nijmegen, The Netherlands), the ILK Research Group
(Tilburg University, The Netherlands) and the CLiPS Research Centre
(University of Antwerp, Belgium).
If you do scientific research in NLP, timbl will likely be of use to you.
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