LaMachine is a unified software distribution for Natural Language Processing

We integrate numerous open-source NLP tools, programming libraries, web-services
and web-applications in a single Virtual Research Environment
that can be installed on a wide variety of machines

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Get Started.

LaMachine comes in various flavours, it can be installed as a Virtual Machine, a Docker container, or directly on your Linux or Mac OS X system.

Start the bootstrap procedure by running a single command and let it guide you through your custom build, or just use one of our pre-built images with Vagrant or Docker!

Your Operating System?
Linux, BSD, or Mac OS X
Windows 10 / 2016
Older Windows
Custom build or pre-built?
Own build (recommend)
Pre-built Docker image
Pre-built VM image
Open a terminal and run the following:
bash <(curl -s https://raw.githubusercontent.com/proycon/LaMachine/master/bootstrap.sh)
Open a terminal and run the above command to get started!

About LaMachine

The software included in LaMachine tends to be highly specialised and generally depends on a lot of other interdependent software. Installing all this software can be a daunting task, compiling it from scratch even more so. LaMachine attempts to make this process easier by offering pre-built recipes for a wide variety of systems, whether it is on your home computer or whether you are setting up a dedicated production environment, LaMachine will safe you a lot of work.

We address various audiences; the bulk of the software is geared towards data scientists who are not afraid of the command line and some programming. We give you the instruments and it is up to you to yield them. However, we also attempt to accommodate researchers that require more high-level interfaces by incorporating webservices and websites that expose some of the functionality to a larger audience.

Software

  • by the Centre for Language and Speech Technology, Radboud University Nijmegen
    • Timbl - Tilburg Memory Based Learner (previously by TiCC, Tilburg University)
    • Ucto - A rule-based tokeniser supporting multiple languages
    • Frog - Frog is an integration of various memory-based natural language processing (NLP) modules developed for Dutch. It can do Part-of-Speech tagging, lemmatisation, named entity recogniton, shallow parsing, dependency parsing and morphological analysis.
    • Mbt - Memory-based Tagger
    • Wopr - Memory-based Word Predictor
    • FoLiA-tools - Command line tools for working with the FoLiA format
    • PyNLPl - Python Natural Language Processing Library
    • Colibri Core - Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way.
    • C++ libraries - ticcutils, libfolia
    • Python bindings - python-ucto, python-frog, python-timbl
    • CLAM - Quickly build RESTful webservices
    • Gecco - Generic Environment for Context-Aware Correction of Orthography (powers Valkuil)
    • Toad - Trainer Of All Data, training tools for Frog
    • foliadocserve - FoLiA Document Server
    • FLAT - FoLiA Linguistic Annotation Tool
    • BabelEnte - An entity extractor, translator and evaluator that uses BabelFy
    • Valkuil - A context-aware spelling corrector for Dutch
    • TicclTools - Tools that together constitute the bulk of TICCL: Text Induced Corpus-Cleanup.
    • PICCL - Pipelines for spelling correction and OCR post-correction system, implements TICCL (also by Tilburg University)
  • by the University of Groningen
    • Alpino, a dependency parser and tagger for Dutch
  • by Utrecht University
    • T-scan - T-scan is a Dutch text analytics tool for readability prediction (initially developed at TiCC, Tilburg University)
  • Major third party software (not exhaustive!)

Documentation

For further documentation, please read the README and the the Contributor Guidelinesfor technical details.

Contribute

LaMachine is open to participation by other open-source NLP software! Please read our Contributor Guidelines.

Support

If you have a problem, please report it on our Issue Tracker.