The Tilburg Memory Based Learner, TiMBL, is an open source 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. It is a core component of various NLP software systems such as MBT (memory-based tagger generator), Frog (Dutch morpho-syntactic analyzer), Valkuil.net (Dutch context-sensitive spelling corrector), and SoothSayer (Dutch word completion).
TiMBL is an an open source software package implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision-tree approximation of IB1-IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases.
Memory-Based Learning (MBL) is a machine-learning method applicable to a wide range of tasks in Natural Language Processing (NLP).
TiMBL is a product of the ILK Research Group (Tilburg University, The Netherlands) and the CLiPS Research Centre (University of Antwerp, Belgium).
- Multi-CPU support.
- Fast, decision-tree-based implementation of k-nearest neighbor classification.
- Implementations of IB1 and IB2, IGTree, TRIBL, and TRIBL2 algorithms.
- Similarity metrics: Overlap, MVDM, Jeffrey Divergence, Dot product, Cosine.
- Per-value similarity metrics: Levenshtein, Dice coefficient.
- Feature weighting metrics: information gain, gain ratio, chi squared, shared variance.
- 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.
- Handles user-defined example weighting.
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