Generic Environment for Context-Aware Correction of Orthography

spelling, corrector, spell, check, nlp, computational_linguistics, rest, python, spelling-correction
pip install Gecco==0.3.0


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======================================================================== GECCO - Generic Environment for Context-Aware Correction of Orthography

by Maarten van Gompel
Centre for Language and Speech Technology, Radboud University Nijmegen
Sponsored by Revisely (
Licensed under the GNU Public License v3

Gecco is a generic modular and distributed framework for spelling correction. Aimed to build a complete context-aware spelling correction system given your own data set. Most modules will be language-independent and trainable from a source corpus. Training is explicitly included in the framework. The framework aims to easily extendible, modules can be written in Python 3. Moreover, the framework is scalable and can be distributed over multiple servers.

Given an input text, Gecco will add various suggestions for correction.

The system can be invoked from the command-line, as a Python binding, as a RESTful webservice, or through the web application (two interfaces).


  • Generic built-in modules:
    • Confusible Module
      • A confusible module is able to discern which version of often confused word is correct given the context. For example, the words "then" and "than" are commonly confused in English.
      • Your configuration should specify between which confusibles the module disambiguates.
      • The module is implemented using the IGTree classifier (a k-Nearest Neighbour approximation) in Timbl.
    • Suffix Confusible Module
      • A variant of the confusible module that checks commonly confused morphological suffixes, rather than words.
      • Your configuration should specify between which suffixes the module disambiguates
      • The module is implemented using the IGTree classifier (a k-Nearest Neighbour approximation) in Timbl.
    • Language Model Module
      • A language model predicts what words are likely to follow others, similar to predictive typing applications commonly found on smartphones.
      • The module is implemented using the IGTree classifier (a k-Nearest Neighbour approximation) in Timbl.
    • Aspell Module
      • Aspell is open-source lexicon-based software for spelling correction. This module enables aspell to be used from gecco. This is not a context-sensitive method.
    • Hunspell Module
      • Hunspell is open-source lexicon-based software for spelling correction. This module enables hunspell to be used from gecco. This is not a context-sensitive method.
    • Lexicon Module
      • The lexicon module enables you to automatically generate a lexicon from corpus data and use it. This is not a context-sensitive method.
      • Typed words are matched against the lexicon and the module will come with suggestions within a certain Levenshtein distance.
    • Errorlist Module
      • The errorlist module is a very simple module that checks whether a word is in a known error list, and if so, provides the suggestions from that list. This is not a context-sensitive method.
    • Split Module
      • The split module detects words that are split but should be written together.
      • Implemented using Colibri Core
    • Runon Module
      • The runon module detects words that are written as one but should be split.
      • Implemented using Colibri Core
    • Punctuation & Recase Module
      • The punctuation & recase module attempts to detect missing punctuation, superfluous punctuation, and missing capitals.
      • The module is implemented using the IGTree classifier (a k-Nearest Neighbour approximation) in Timbl.
  • Modules suggested but not implemented yet:
    • Language Detection Module
      • (Not written yet, option for later)
    • Sound-alike Module
      • (Not written yet, option for later)


  • Easily extendible by adding modules using the gecco module API
  • Language independent
  • Built-in training pipeline (given corpus input): Create models from sources
  • Built-in testing pipeline (given an error-annotated test corpus), returns report of evaluation metrics per module
  • Distributed, Multithreaded & Scalable:
    • Load balancing: backend servers can run on multiple hosts, master process distributes amongst these
    • Multithreaded, modules can be invoked in parallel, module servers themselves may be multithreaded too
  • Input and output is FoLiA XML (
    • Automatic input conversion from plain text using ucto

Gecco is the successor of and


Gecco relies on a large number of dependencies, including but not limited to:


To install Gecco, we strongly recommend you to use our LaMachine distribution, which can be obtained from .

LaMachine includes Gecco and can be run in multiple ways: as a virtual machine, as a docker app, or as a compilation script setting up a Python virtual environment.

Gecco uses memory-based technologies, and depending on the models you train, may take up considerable memory. Therefore we recommend at least 16GB RAM, training may require even more. For various modules, model size may be reduced by increasing frequency thresholds, but this will come at the cost of reduced accuracy.

Gecco will only run on POSIX-complaint operating systems (i.e. Linux, BSD, Mac OS X), not on Windows.


To build an actual spelling correction system, you need to have corpus sources and create a gecco configuration that enable the modules you desire with the parameters you want.

A Gecco system consists of a configuration, either in the form of a simple Python script or an external YAML configuration file.

Example YAML configuration:

name: fowlt
path: /path/to/fowlt
language: en
    - module: gecco.modules.confusibles.TIMBLWordConfusibleModule
      id: confusibles
        - train.txt
        - confusible.model
      confusibles: [then,than]

To list all available modules and the parameters they may take, run gecco --helpmodules.

Alternatively, the configuration can be done in Python directly, in which case the script will be the tool that exposes all functionality:

from gecco import Corrector
from gecco.modules.confusibles import TIMBLWordConfusibleModule

corrector = Corrector(id="fowlt", root="/path/to/fowlt/")
corrector.append( TIMBLWordConfusibleModule("thenthan", source="train.txt",test_crossvalidate=True,test=0.1,tune=0.1,model="confusibles.model", confusible=('then','than')))
corrector.append( TIMBLWordConfusibleModule("its", source="train.txt",test_crossvalidate=True,test=0.1,tune=0.1,model="confusibles.model", confusible=('its',"it's")))
corrector.append( TIMBLWordConfusibleModule("errorlist", source="errorlist.txt",model="errorlist.model", servers=[("blah",1234),("blah2",1234)]  )
corrector.append( TIMBLWordConfusibleModule("lexicon", source=["lexicon.txt","lexicon2.txt"],model=["lexicon.model","lexicon2.model"], servers=[("blah",1235)]  )

It is recommended to adopt a file/directory structure as described below. If you plan on using multiple hosts, you should store it on a shared network drive so all hosts can access the models:

  • yourconfiguration.yml
  • sources/
  • models/

An example system spelling correction system for English is provided with Gecco and resides in the example/ directory.

Server setup

gecco <yourconfig.yml> run <input.folia.xml> is executed to process a given FoLiA document or plaintext document, it starts a master process that will invoke all the modules, which may be distributed over multiple servers. If multiple server instances of the same module are available, the load will be distributed over them. Output will be delivered in the FoLiA XML format and will contain suggestions for correction.

To start module servers on a host, issue gecco <yourconfig.yml> startservers. You can optionally specify which servers you want to start, if you do not want to start all. You can start servers multiple times, either on the same or on multiple hosts. The master process will distribute the load amongst all servers.

To stop the servers, run gecco <yourconfig.yml> stopservers on each host that has servers running. A list of all running servers can be obtained by gecco <yourconfig.yml> listservers.

Modules can also run locally within the master process rather than as servers, this is done by either by adding local: true in the configuration, or by adding the --local option when starting a run. But this will have a significant negative impact on performance and should therefore be avoided.


Gecco Architecture

Command line usage

Invoke all gecco functionality through a single command line tool

$ gecco myconfig.yml [subcommand]


$ [subcommand]


usage: gecco [-h]

Gecco is a generic, scalable and modular spelling correction framework

    run                 Run the spelling corrector on the specified input file
    startservers        Starts all the module servers that are configured to
                        run on the current host. Issue once for each host.
    stopservers         Stops all the module servers that are configured to
                        run on the current host. Issue once for each host.
    listservers         Lists all the module servers on all hosts
    startserver         Start one module's server on the specified port, use
                        'startservers' instead
    train               Train modules
    evaluate            Runs the spelling corrector on input data and compares
                        it to reference data, produces an evaluation report
    reset               Reset modules, deletes all trained models that have
                        sources. Issue prior to train if you want to start

Vital documentation regarding all modules and the settings they take can be obtained through:

$ gecco --helpmodules

Gecco as a webservice

RESTUL webservice access will be available through CLAM. We are still working on better integration of this in Gecco. FOr now, an example implementation of this can be seen here:

Gecco as a web-application

A web-application will eventually be available, modelled after