modest_bauwenst 2026.3.1

MoDeST: a Morphological Decomposition & Segmentation Trove.

MoDeST: a Morphological Decomposition & Segmentation Trove

The point of MoDeST is two-fold:

1. Provide a general object-oriented Python interface to access morphological decompositions and segmentations;
2. Host morphological datasets generated by smaller research groups that would otherwise have a hard time being found.

Morphological decomposition is the task of recognising which building blocks a word was originally constructed from. These building blocks are its morphemes. As an example, the Dutch derivation isometrisch ("isometric") can be decomposed into the morphemes iso, meter and isch.

Morphological segmentation is the task of isolating the substrings of a word that correspond to its morphemes. These substrings are called morphs. In the above example, the segmentation would be iso/metr/ic.

pip install modest[all]

Languages and Datasets

The supported languages are simply under modest.languages, so the list will not be reproduced here. The list of datasets roughly coincides with the downloaders under modest.datasets. Currently, the package supports:

• CELEX
• MorphyNet
• MorphoChallenge2010
• CompoundPiece

Example

Yes, it really is this easy, with full type checking and autocompletion by your IDE:

from modest.languages.english import English_Celex, English_MorphoChallenge2010, English_MorphyNet_Inflections

for item in English_Celex().generate():
    print(item.word, "should be segmented as", item.segment(), "which derives from", item.decompose())

Repo layout

Currently, the repo looks as follows:

data/                ---> Datasets hosted specifically by MoDeST on GitHub. Will NOT be downloaded when you install the package.
src/modest/          ---> All source code for the Python package that will be installed in your interpreter.
    languages/       ---> Per-language definitions of the dataset classes users will interact with.
    datasets/        ---> Support code for pulling in remote data, and reading raw examples from the resulting files.
    formats/         ---> Support code for turning raw examples into objects. Mainly used for parsing tags, whose format is independent of how they are stored (TSV, XML, JSON, ...).
    interfaces/      ---> Declarations of the interfaces users will interact with.
    transformations/ ---> Operations performed on datasets.

Currently, every language has its own file under languages/. The assumption is that the datasets pertaining to one language are sufficiently encapsulated that this will not clutter the imports from such a file. There are two arguments in favour of going from languages/{language}.py to instead languages/{language}/{dataset}.py:

1. Autocompletion for the last . of the import suggests exactly the list of available datasets for that language;
2. You do not have to have all the packages installed required to download/build all the datasets for a language if you only need one. (However, realistically, since MoDeST is for final datasets rather than making datasets, the code for pulling them should not be that complicated.)