Qumin: Quantitative modelling of inflection

Qumin (QUantitative Modelling of INflection) is a collection of scripts for the computational modelling of the inflectional morphology of languages. It was developed by me (Sacha Beniamine) for my PhD, which was supervised by Olivier Bonami .

The documentation has moved to ReadTheDocs at: https://qumin.readthedocs.io/

For more detail, you can refer to my dissertation (in French):

Sacha Beniamine. Classifications flexionnelles. Étude quantitative des structures de paradigmes. Linguistique. Université Sorbonne Paris Cité - Université Paris Diderot (Paris 7), 2018. Français.

Quick Start


First, open the terminal and navigate to the folder where you want the Qumin code. Clone the repository from github:

git clone https://github.com/XachaB/Qumin.git

Make sure to have all the python dependencies installed. The dependencies are listed in environment.yml. A simple solution is to use conda and create a new environment from the environment.yml file:

conda env create -f environment.yml

There is now a new conda environment named Qumin. It needs to be activated before using any Qumin script:

conda activate Qumin


The scripts expect full paradigm data in phonemic transcription, as well as a feature key for the transcription.

To provide a data sample in the correct format, Qumin includes a subset of the French flexique lexicon, distributed under a Creative Commons Attribution-NonCommercial-ShareAlike license.

For Russian nouns, see the Inflected lexicon of Russian Nouns in IPA notation.



Alternation patterns serve as a basis for all the other scripts. The algorithm to find the patterns was presented in: Sacha Beniamine. Un algorithme universel pour l’abstraction automatique d’alternances morphophonologiques 24e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), Jun 2017, Orléans, France. 2 (2017), 24e Conférence sur le Traitement Automatique des Langues Naturelles.

Computing automatically aligned patterns for paradigm entropy or macroclass:

bin/$ python3 find_patterns.py <paradigm.csv> <segments.csv>

Computing automatically aligned patterns for lattices:

bin/$ python3 find_patterns.py -d -o <paradigm.csv> <segments.csv>


To visualize the microclasses and their similarities, you can use the new script microclass_heatmap.py:

Computing a microclass heatmap:

bin/$ python3 microclass_heatmap.py <paradigm.csv> <output_path>

Computing a microclass heatmap, comparing with class labels:

bin/$ python3 microclass_heatmap.py -l  <labels.csv> -- <paradigm.csv> <output_path>

The labels file is a csv file. The first column give lexemes names, the second column provides inflection class labels. This allows to visually compare a manual classification with pattern-based similarity. This script relies heavily on seaborn’s clustermap function.

Paradigm entropy

This script was used in:

Computing entropies from one cell

bin/$ python3 calc_paradigm_entropy.py -n 1 -- <patterns.csv> <paradigm.csv> <segments.csv>

Computing entropies from two cells (you can specify any number of predictors, e.g. -n 1 2 3 works too)

bin/$ python3 calc_paradigm_entropy.py -n 2 -- <patterns.csv> <paradigm.csv> <segments.csv>

Add a file with features to help prediction (for example gender – features will be added to the known information when predicting)

bin/$ python3 calc_paradigm_entropy.py -n 2 --features <features.csv> -- <patterns.csv> <paradigm.csv> <segments.csv>

Macroclass inference

Our work on automatical inference of macroclasses was published in Beniamine, Sacha, Olivier Bonami, and Benoît Sagot. “Inferring Inflection Classes with Description Length.” Journal of Language Modelling (2018).

Inferring macroclasses

bin/$ python3 find_macroclasses.py  <patterns.csv> <segments.csv>


This script was used in:

Inferring a lattice of inflection classes, with html output

bin/$ python3 make_lattice.py --html <patterns.csv> <segments.csv>