How to Install and Uninstall timbl Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: November 07,2024

1. Install "timbl" package

This tutorial shows how to install timbl on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                timbl
            
                    
    
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2. Uninstall "timbl" package

This is a short guide on how to uninstall timbl on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                timbl
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the timbl package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: timbl
Priority: optional
Section: universe/science
Installed-Size: 158
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Team
Architecture: amd64
Version: 6.4.6-1build1
Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 5.2), libticcutils2v5, libtimbl4
Filename: pool/universe/t/timbl/timbl_6.4.6-1build1_amd64.deb
Size: 53116
MD5sum: c4ec87362d7150475d47d937d7cea069
SHA1: 37d25e32800655e585288b812aec8f0865731652
SHA256: a1b2086503e7e5cdf4a824920e8be1e2216d45e66164e9cbb5a4d553131aac48
Description-en: Tilburg Memory Based Learner
Memory-Based Learning (MBL) is a machine-learning method applicable to a wide
range of tasks in Natural Language Processing (NLP).
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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.
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TiMBL's features are:
* 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;
* 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;
* and Handles user-defined example weighting.
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TiMBL is a product of the ILK Research Group (Tilburg University, The
Netherlands) and the CLiPS Research Centre (University of Antwerp, Belgium).
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If you do scientific research in NLP, timbl will likely be of use to you.
Description-md5: 44b1cc539f7f47c2c93bc4bbfebccaec
Homepage: http://ilk.uvt.nl/timbl/
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu