mlconjug3 3.8.1

A Python library to conjugate French, English, Spanish, Italian, Portuguese and Romanian verbs using Machine Learning techniques.

MLCONJUG3

A Python library to conjugate verbs in French, English, Spanish, Italian, Portuguese and Romanian (more soon) using Machine Learning techniques.

Any verb in one of the supported language can be conjugated, as the module contains a Machine Learning model of how the verbs behave.

Even completely new or made-up verbs can be successfully conjugated in this manner.

The supplied pre-trained models are composed of:

• a binary feature extractor,

• a feature selector using Linear Support Vector Classification,

• a classifier using Stochastic Gradient Descent.

MLConjug3 uses scikit-learn to implement the Machine Learning algorithms.

Users of the library can use any compatible classifiers from scikit-learn to modify and retrain the models.

The training data for the french model is based on Verbiste https://perso.b2b2c.ca/~sarrazip/dev/verbiste.html .

The training data for English, Spanish, Italian, Portuguese and Romanian was generated using unsupervised learning techniques using the French model as a model to query during the training.

Warning

MLCONJUG3 now only supports Python 3.x as Python 2.x has been deprecated in 2020.

• Free software: MIT license

• Documentation: https://sekoudiaonlp.github.io/mlconjug3/index.html.

Supported Languages

• French

• English

• Spanish

• Italian

• Portuguese

• Romanian

Features

• Easy to use API.

• Includes pre-trained models with 99% + accuracy in predicting conjugation class of unknown verbs.

• Easily train new models or add new languages.

• Easily integrate MLConjug in your own projects.

• Can be used as a command line tool.

Academic publications citing mlconjug

• Ali Malik and Mike Wu and Vrinda Vasavada and Jinpeng Song and John Mitchell and Noah D. Goodman and Chris Piech.

  “Generative Grading Neural Approximate Parsing for Automated Student Feedback”.

  Proceedings of the 34th AAAI conference on Artificial Intelligence, 2019.

Software projects using mlconjug

• NLP-Suite

  NLP Suite is a package of tools designed for non-specialists, for scholars with no knowledge or little knowledge of Natural Language Processing.
• Gender Bias Visualization

  This project offers tools to visualize the gender bias in pre-trained language models to better understand the prejudices in the data.
• Text Adaptation To Context

  This project uses language models to generate text that is well suited to the type of publication.
• verbecc-svc

  Dockerized microservice with REST API for conjugation of any verb in French and Spanish.
• nvhtml

  A tool to Manage and tansform HTML documents.
• twitter-bot

  Tweets the words of the French language. Largely inspired by the @botducul (identical lexicon, but code in Python) and the @botsupervnr.

  Posts on @botduslip. Stores the position of the last tweeted word in a Redis database.
• verb-form-helper

  This project offers a tool to help learn differnt verbal forms.
• NLP Tasks

  A collection of common NLP tasks such as dataset parsing and explicit semantic extraction.
• Facemask Detection

  This project offers a model which recognizes covid-19 masks.
• Bad Excuses for Zoom Abuses

  Need an excuse for why you can’t show up in your Zoom lectures? Just generate one here!
• NLP

  Repository to store Natural Language Processing models.
• Virtual Assistant

  This is a simple virtual assistant. With it, you can search the Internet, access websites, open programs, and more using just your voice.

  This virtual assistant supports the English and Portuguese languages and has many settings that you can adjust to your liking.
• Bad Advice

  This python module responds to yes or no questions. It dishes out its advice at random.

  Disclaimer: Do not actually act on this advice ;)
• Spanish Conjugations Quiz

  Python+Flask web app that uses mlconjug to dynamically generate foreign language conjugation questions.
• Silver Rogue DF

  A dwarf-fortress adventure mode-inspired rogue-like Pygame Python3 game.

BibTeX

If you want to cite mlconjug3 in an academic publication use this citation format:

@article{mlconjug3,
  title={mlconjug3},
  author={Sekou Diao},
  journal={GitHub. Note: https://github.com/SekouDiaoNlp/mlconjug3 Cited by},
  year={2021}
}

Credits

This package was created with the help of Verbiste and scikit-learn.

The logo was designed by Zuur.