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Datasets:
unimorph
/
universal_morphologies

Tasks:
Token Classification
Text Classification
Sub-tasks:
multi-class-classification
multi-label-classification
Languages:
Adyghe
Old English (ca. 450-1100)
Arabic
Size:
10K<n<100K
Tags:
morphology
License:
Dataset card Files Community
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universal_morphologies / README.md
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - found
languages:
  ady:
    - ady
  ang:
    - ang
  ara:
    - ar
  arn:
    - arn
  ast:
    - ast
  aze:
    - az
  bak:
    - ba
  bel:
    - be
  ben:
    - bn
  bod:
    - bo
  bre:
    - br
  bul:
    - bg
  cat:
    - ca
  ces:
    - cs
  chu:
    - cu
  ckb:
    - ckb
  cor:
    - kw
  crh:
    - crh
  csb:
    - csb
  cym:
    - cy
  dan:
    - da
  deu:
    - de
  dsb:
    - dsb
  ell:
    - el
  eng:
    - en
  est:
    - et
  eus:
    - eu
  fao:
    - fo
  fas:
    - fa
  fin:
    - fi
  fra:
    - fr
  frm:
    - frm
  fro:
    - fro
  frr:
    - frr
  fry:
    - fy
  fur:
    - fur
  gal:
    - gal
  gla:
    - gd
  gle:
    - ga
  glv:
    - gv
  gmh:
    - gmh
  gml:
    - gml
  got:
    - got
  grc:
    - grc
  hai:
    - hai
  hbs:
    - sh
  heb:
    - he
  hin:
    - hi
  hun:
    - hu
  hye:
    - hy
  isl:
    - is
  ita:
    - it
  izh:
    - izh
  kal:
    - kl
  kan:
    - kn
  kat:
    - ka
  kaz:
    - kk
  kbd:
    - kbd
  kjh:
    - kjh
  klr:
    - klr
  kmr:
    - kmr
  krl:
    - krl
  lat:
    - la
  lav:
    - lv
  lit:
    - lt
  liv:
    - liv
  lld:
    - lld
  lud:
    - lud
  mkd:
    - mk
  mlt:
    - mt
  mwf:
    - mwf
  nap:
    - nap
  nav:
    - nv
  nds:
    - nds
  nld:
    - nl
  nno:
    - nn
  nob:
    - nb
  oci:
    - oc
  olo:
    - olo
  osx:
    - osx
  pol:
    - pl
  por:
    - pt
  pus:
    - ps
  que:
    - qu
  ron:
    - ro
  rus:
    - ru
  san:
    - sa
  sga:
    - sga
  slv:
    - sl
  sme:
    - sme
  spa:
    - es
  sqi:
    - sq
  swc:
    - swc
  swe:
    - sv
  syc:
    - syc
  tat:
    - tt
  tel:
    - te
  tgk:
    - tg
  tuk:
    - tk
  tur:
    - tr
  ukr:
    - uk
  urd:
    - ur
  uzb:
    - uz
  vec:
    - vec
  vep:
    - vep
  vot:
    - vot
  xcl:
    - xcl
  xno:
    - xno
  yid:
    - yi
  zul:
    - zu
licenses:
  - cc-by-sa-3-0
multilinguality:
  - monolingual
size_categories:
  ady:
    - 1K<n<10K
  ang:
    - 1K<n<10K
  ara:
    - 1K<n<10K
  arn:
    - n<1K
  ast:
    - n<1K
  aze:
    - n<1K
  bak:
    - 1K<n<10K
  bel:
    - 1K<n<10K
  ben:
    - n<1K
  bod:
    - 1K<n<10K
  bre:
    - n<1K
  bul:
    - 1K<n<10K
  cat:
    - 1K<n<10K
  ces:
    - 1K<n<10K
  chu:
    - n<1K
  ckb:
    - n<1K
  cor:
    - n<1K
  crh:
    - 1K<n<10K
  csb:
    - n<1K
  cym:
    - n<1K
  dan:
    - 1K<n<10K
  deu:
    - 10K<n<100K
  dsb:
    - n<1K
  ell:
    - 10K<n<100K
  eng:
    - 10K<n<100K
  est:
    - n<1K
  eus:
    - n<1K
  fao:
    - 1K<n<10K
  fas:
    - n<1K
  fin:
    - 10K<n<100K
  fra:
    - 1K<n<10K
  frm:
    - n<1K
  fro:
    - 1K<n<10K
  frr:
    - n<1K
  fry:
    - n<1K
  fur:
    - n<1K
  gal:
    - n<1K
  gla:
    - n<1K
  gle:
    - 1K<n<10K
  glv:
    - n<1K
  gmh:
    - n<1K
  gml:
    - n<1K
  got:
    - n<1K
  grc:
    - 1K<n<10K
  hai:
    - n<1K
  hbs:
    - 10K<n<100K
  heb:
    - n<1K
  hin:
    - n<1K
  hun:
    - 10K<n<100K
  hye:
    - 1K<n<10K
  isl:
    - 1K<n<10K
  ita:
    - 10K<n<100K
  izh:
    - n<1K
  kal:
    - n<1K
  kan:
    - n<1K
  kat:
    - 1K<n<10K
  kaz:
    - n<1K
  kbd:
    - n<1K
  kjh:
    - n<1K
  klr:
    - n<1K
  kmr:
    - 10K<n<100K
  krl:
    - n<1K
  lat:
    - 10K<n<100K
  lav:
    - 1K<n<10K
  lit:
    - 1K<n<10K
  liv:
    - n<1K
  lld:
    - n<1K
  lud:
    - n<1K
  mkd:
    - 10K<n<100K
  mlt:
    - n<1K
  mwf:
    - n<1K
  nap:
    - n<1K
  nav:
    - n<1K
  nds:
    - n<1K
  nld:
    - 1K<n<10K
  nno:
    - 1K<n<10K
  nob:
    - 1K<n<10K
  oci:
    - n<1K
  olo:
    - 10K<n<100K
  osx:
    - n<1K
  pol:
    - 10K<n<100K
  por:
    - 1K<n<10K
  pus:
    - n<1K
  que:
    - 1K<n<10K
  ron:
    - 1K<n<10K
  rus:
    - 10K<n<100K
  san:
    - n<1K
  sga:
    - n<1K
  slv:
    - 1K<n<10K
  sme:
    - 1K<n<10K
  spa:
    - 1K<n<10K
  sqi:
    - n<1K
  swc:
    - n<1K
  swe:
    - 10K<n<100K
  syc:
    - n<1K
  tat:
    - 1K<n<10K
  tel:
    - n<1K
  tgk:
    - n<1K
  tuk:
    - n<1K
  tur:
    - 1K<n<10K
  ukr:
    - 1K<n<10K
  urd:
    - n<1K
  uzb:
    - n<1K
  vec:
    - n<1K
  vep:
    - 1K<n<10K
  vot:
    - n<1K
  xcl:
    - 1K<n<10K
  xno:
    - n<1K
  yid:
    - n<1K
  zul:
    - n<1K
source_datasets:
  - original
task_categories:
  - structure-prediction
  - text-classification
task_ids:
  - multi-class-classification
  - multi-label-classification
  - structure-prediction-other-morphology

Dataset Card for [Dataset Name]

Table of Contents

Dataset Description

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

[More Information Needed]

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

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @yjernite for adding this dataset.