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Dataset Summary

The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema. The specification of the schema is described in Sylak-Glassman (2016).

Supported Tasks and Leaderboards

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Languages

The current version of the UniMorph dataset covers 110 languages.

Dataset Structure

Data Instances

Each data instance comprises of a lemma and a set of possible realizations with morphological and meaning annotations. For example:

{'forms': {'Aktionsart': [[], [], [], [], []],
  'Animacy': [[], [], [], [], []],
  ...
  'Finiteness': [[], [], [], [1], []],
  ...
  'Number': [[], [], [0], [], []],
  'Other': [[], [], [], [], []],
  'Part_Of_Speech': [[7], [10], [7], [7], [10]],
  ...
  'Tense': [[1], [1], [0], [], [0]],
  ...
  'word': ['ablated', 'ablated', 'ablates', 'ablate', 'ablating']},
 'lemma': 'ablate'}

Data Fields

Each instance in the dataset has the following fields:

  • lemma: the common lemma for all all_forms
  • forms: all annotated forms for this lemma, with:
    • word: the full word form
    • [category]: a categorical variable denoting one or several tags in a category (several to represent composite tags, originally denoted with A+B). The full list of categories and possible tags for each can be found here

Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

Thanks to @yjernite for adding this dataset.

Models trained or fine-tuned on universal_morphologies

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