unimorph/universal_morphologies

---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ady
- ang
- ar
- arn
- ast
- az
- ba
- be
- bg
- bn
- bo
- br
- ca
- ckb
- crh
- cs
- csb
- cu
- cy
- da
- de
- dsb
- el
- en
- es
- et
- eu
- fa
- fi
- fo
- fr
- frm
- fro
- frr
- fur
- fy
- ga
- gal
- gd
- gmh
- gml
- got
- grc
- gv
- hai
- he
- hi
- hu
- hy
- is
- it
- izh
- ka
- kbd
- kjh
- kk
- kl
- klr
- kmr
- kn
- krl
- kw
- la
- liv
- lld
- lt
- lud
- lv
- mk
- mt
- mwf
- nap
- nb
- nds
- nl
- nn
- nv
- oc
- olo
- osx
- pl
- ps
- pt
- qu
- ro
- ru
- sa
- sga
- sh
- sl
- sme
- sq
- sv
- swc
- syc
- te
- tg
- tk
- tr
- tt
- uk
- ur
- uz
- vec
- vep
- vot
- xcl
- xno
- yi
- zu
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- token-classification
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
paperswithcode_id: null
pretty_name: UniversalMorphologies
tags:
- morphology
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15: CONJ
16: NUM
17: PART
18: INTJ
- name: Person
sequence:
class_label:
names:
0: '0'
1: '1'
2: '2'
3: '3'
4: '4'
5: INCL
6: EXCL
7: PRX
8: OBV
- name: Polarity
sequence:
class_label:
names:
0: POS
1: NEG
- name: Politeness
sequence:
class_label:
names:
0: INFM
1: FORM
2: ELEV
3: HUMB
4: POL
5: AVOID
6: LOW
7: HIGH
8: STELEV
9: STSUPR
10: LIT
11: FOREG
12: COL
- name: Possession
sequence:
class_label:
names:
0: ALN
1: NALN
2: PSS1S
3: PSS2S
4: PSS2SF
5: PSS2SM
6: PSS2SINFM
7: PSS2SFORM
8: PSS3S
9: PSS3SF
10: PSS3SM
11: PSS1D
12: PSS1DI
13: PSS1DE
14: PSS2D
15: PSS2DM
16: PSS2DF
17: PSS3D
18: PSS3DF
19: PSS3DM
20: PSS1P
21: PSS1PI
22: PSS1PE
23: PSS2P
24: PSS2PF
25: PSS2PM
26: PSS3PF
27: PSS3PM
- name: Switch_Reference
sequence:
class_label:
names:
0: SS
1: SSADV
2: DS
3: DSADV
4: OR
5: SIMMA
6: SEQMA
7: LOG
- name: Tense
sequence:
class_label:
names:
0: PRS
1: PST
2: FUT
3: IMMED
4: HOD
5: 1DAY
6: RCT
7: RMT
- name: Valency
sequence:
class_label:
names:
0: IMPRS
1: INTR
2: TR
3: DITR
4: REFL
5: RECP
6: CAUS
7: APPL
- name: Voice
sequence:
class_label:
names:
0: ACT
1: MID
2: PASS
3: ANTIP
4: DIR
5: INV
6: AGFOC
7: PFOC
8: LFOC
9: BFOC
10: ACFOC
11: IFOC
12: CFOC
- name: Other
sequence: string
splits:
- name: train
num_bytes: 7152507
num_examples: 566
download_size: 1581402
dataset_size: 7152507
config_names:
- ady
- ang
- ara
- arn
- ast
- aze
- bak
- bel
- ben
- bod
- bre
- bul
- cat
- ces
- chu
- ckb
- cor
- crh
- csb
- cym
- dan
- deu
- dsb
- ell
- eng
- est
- eus
- fao
- fas
- fin
- fra
- frm
- fro
- frr
- fry
- fur
- gal
- gla
- gle
- glv
- gmh
- gml
- got
- grc
- hai
- hbs
- heb
- hin
- hun
- hye
- isl
- ita
- izh
- kal
- kan
- kat
- kaz
- kbd
- kjh
- klr
- kmr
- krl
- lat
- lav
- lit
- liv
- lld
- lud
- mkd
- mlt
- mwf
- nap
- nav
- nds
- nld
- nno
- nob
- oci
- olo
- osx
- pol
- por
- pus
- que
- ron
- rus
- san
- sga
- slv
- sme
- spa
- sqi
- swc
- swe
- syc
- tat
- tel
- tgk
- tuk
- tur
- ukr
- urd
- uzb
- vec
- vep
- vot
- xcl
- xno
- yid
- zul
---

# Dataset Card for [Dataset Name]

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** [UniMorph Homepage](https://unimorph.github.io/)
- **Repository:** [List of UniMorph repositories](https://github.com/unimorph)
- **Paper:** [The Composition and Use of the Universal Morphological Feature Schema (UniMorph Schema)](https://unimorph.github.io/doc/unimorph-schema.pdf)
- **Point of Contact:** [Arya McCarthy](mailto:[e-mail])

### 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](https://github.com/unimorph/unimorph.github.io/blob/master/unimorph-schema-json/dimensions-to-features.json)

### 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](https://github.com/yjernite) for adding this dataset.

原始信息汇总

数据集概述

基本信息

• 数据集名称: UniversalMorphologies
• 语言: 包含多种语言，具体包括：ady, ang, ar, arn, ast, az, ba, be, bg, bn, bo, br, ca, ckb, crh, cs, csb, cu, cy, da, de, dsb, el, en, es, et, eu, fa, fi, fo, fr, frm, fro, frr, fur, fy, ga, gal, gd, gmh, gml, got, grc, gv, hai, he, hi, hu, hy, is, it, izh, ka, kbd, kjh, kk, kl, klr, kmr, kn, krl, kw, la, liv, lld, lt, lud, lv, mk, mt, mwf, nap, nb, nds, nl, nn, nv, oc, olo, osx, pl, ps, pt, qu, ro, ru, sa, sga, sh, sl, sme, sq, sv, swc, syc, te, tg, tk, tr, tt, uk, ur, uz, vec, vep, vot, xcl, xno, yi, zu
• 许可证: CC-BY-SA-3.0
• 多语言性: 单语种
• 数据集大小分类: 10K<n<100K, 1K<n<10K, n<1K
• 数据来源: 原始数据
• 任务类别:
  • 标记分类
  • 文本分类
• 任务ID:
  • 多类分类
  • 多标签分类

数据集配置

配置名称: ady

• 特征:
  • lemma: 字符串
  • forms: 序列
    • word: 字符串
    • Aktionsart: 序列
      • 类别标签: STAT, DYN, TEL, ATEL, PCT, DUR, ACH, ACCMP, SEMEL, ACTY
    • Animacy: 序列
      • 类别标签: ANIM, INAN, HUM, NHUM
    • Argument_Marking: 序列
      • 类别标签: ARGNO1S, ARGNO2S, ARGNO3S, ARGNO1P, ARGNO2P, ARGNO3P, ARGAC1S, ARGAC2S, ARGAC3S, ARGAC1P, ARGAC2P, ARGAC3P, ARGAB1S, ARGAB2S, ARGAB3S, ARGAB1P, ARGAB2P, ARGAB3P, ARGER1S, ARGER2S, ARGER3S, ARGER1P, ARGER2P, ARGER3P, ARGDA1S, ARGDA2S, ARGDA3S, ARGDA1P, ARGDA2P, ARGDA3P, ARGBE1S, ARGBE2S, ARGBE3S, ARGBE1P, ARGBE2P, ARGBE3P
    • Aspect: 序列
      • 类别标签: IPFV, PFV, PRF, PROG, PROSP, ITER, HAB
    • Case: 序列
      • 类别标签: NOM, ACC, ERG, ABS, NOMS, DAT, BEN, PRP, GEN, REL, PRT, INS, COM, VOC, COMPV, EQTV, PRIV, PROPR, AVR, FRML, TRANS, BYWAY, INTER, AT, POST, IN, CIRC, ANTE, APUD, ON, ONHR, ONVR, SUB, REM, PROXM, ESS, ALL, ABL, APPRX, TERM
    • Comparison: 序列
      • 类别标签: CMPR, SPRL, AB, RL, EQT
    • Definiteness: 序列
      • 类别标签: DEF, INDF, SPEC, NSPEC
    • Deixis: 序列
      • 类别标签: PROX, MED, REMT, REF1, REF2, NOREF, PHOR, VIS, NVIS, ABV, EVEN, BEL
    • Evidentiality: 序列
      • 类别标签: FH, DRCT, SEN, VISU, NVSEN, AUD, NFH, QUOT, RPRT, HRSY, INFER, ASSUM
    • Finiteness: 序列
      • 类别标签: FIN, NFIN
    • Gender: 序列
      • 类别标签: MASC, FEM, NEUT, NAKH1, NAKH2, NAKH3, NAKH4, NAKH5, NAKH6, NAKH7, NAKH8, BANTU1, BANTU2, BANTU3, BANTU4, BANTU5, BANTU6, BANTU7, BANTU8, BANTU9, BANTU10, BANTU11, BANTU12, BANTU13, BANTU14, BANTU15, BANTU16, BANTU17, BANTU18, BANTU19, BANTU20, BANTU21, BANTU22, BANTU23
    • Information_Structure: 序列
      • 类别标签: TOP, FOC
    • Interrogativity: 序列
      • 类别标签: DECL, INT
    • Language_Specific: 序列
      • 类别标签: LGSPEC1, LGSPEC2, LGSPEC3, LGSPEC4, LGSPEC5, LGSPEC6, LGSPEC7, LGSPEC8, LGSPEC9, LGSPEC10
    • Mood: 序列
      • 类别标签: IND, SBJV, REAL, IRR, AUPRP, AUNPRP, IMP, COND, PURP, INTEN, POT, LKLY, ADM, OBLIG, DEB, PERM, DED, SIM, OPT
    • Number: 序列
      • 类别标签: SG, PL, GRPL, DU, TRI, PAUC, GRPAUC, INVN
    • Part_Of_Speech: 序列
      • 类别标签: N, PROPN, ADJ, PRO, CLF, ART, DET, V, ADV, AUX, V.PTCP, V.MSDR, V.CVB, ADP, COMP, CONJ, NUM, PART, INTJ
    • Person: 序列
      • 类别标签: 0, 1, 2, 3, 4, INCL, EXCL, PRX, OBV
    • Polarity: 序列
      • 类别标签: POS, NEG
    • Politeness: 序列
      • 类别标签: INFM, FORM, ELEV, HUMB, POL, AVOID, LOW, HIGH, STELEV, STSUPR, LIT, FOREG, COL
    • Possession: 序列
      • 类别标签: ALN, NALN, PSS1S, PSS2S, PSS2SF, PSS2SM, PSS2SINFM, PSS2SFORM, PSS3S, PSS3SF, PSS3SM, PSS1D, PSS1DI, PSS1DE, PSS2D, PSS2DM, PSS2DF, PSS3D, PSS3DF, PSS3DM, PSS1P, PSS1PI, PSS1PE, PSS2P, PSS2PF, PSS2PM, PSS3PF, PSS3PM
    • Switch_Reference: 序列
      • 类别标签: SS, SSADV, DS, DSADV, OR, SIMMA, SEQMA, LOG
    • Tense: 序列
      • 类别标签: PRS, PST, FUT, IMMED, HOD, 1DAY, RCT, RMT
    • Valency: 序列
      • 类别标签: IMPRS, INTR, TR, DITR, REFL, RECP, CAUS, APPL
    • Voice: 序列
      • 类别标签: ACT, MID, PASS, ANTIP, DIR, INV, AGFOC, PFOC, LFOC, BFOC, ACFOC, IFOC, CFOC
    • Other: 序列
      • 字符串
• 分割:
  • train:
    • num_bytes: 3428235
    • num_examples: 1666
  • download_size: 1008487
  • dataset_size: 3428235

配置名称: ang

• 特征:
  • lemma: 字符串
  • forms: 序列
    • word: 字符串
    • Aktionsart: 序列
      • 类别标签: STAT, DYN, TEL, ATEL, PCT, DUR, ACH, ACCMP, SEMEL, ACTY
    • Animacy: 序列
      • 类别标签: ANIM, INAN, HUM, NHUM
    • Argument_Marking: 序列
      • 类别标签: ARGNO1S, ARGNO2S, ARGNO3S, ARGNO1P, ARGNO2P, ARGNO3P, ARGAC1S, ARGAC2S, ARGAC3S, ARGAC1P, ARGAC2P, ARGAC3P, ARGAB1S, ARGAB2S, ARGAB3S, ARGAB1P, ARGAB2P, ARGAB3P, ARGER1S, ARGER2S, ARGER3S, ARGER1P, ARGER2P, ARGER3P, ARGDA1S, ARGDA2S, ARGDA3S, ARGDA1P, ARGDA2P, ARGDA3P, ARGBE1S, ARGBE2S, ARGBE3S, ARGBE1P, ARGBE2P, ARGBE3P
    • Aspect: 序列
      • 类别标签: IPFV, PFV, PRF, PROG, PROSP, ITER, HAB
    • Case: 序列
      • 类别标签: NOM, ACC, ERG, ABS, NOMS, DAT, BEN, PRP, GEN, REL, PRT, INS, COM, VOC, COMPV, EQTV, PRIV, PROPR, AVR, FRML, TRANS, BYWAY, INTER, AT, POST, IN, CIRC, ANTE, APUD, ON, ONHR, ONVR, SUB, REM, PROXM, ESS, ALL, ABL, APPRX, TERM
    • Comparison: 序列
      • 类别标签: CMPR, SPRL, AB, RL, EQT
    • Definiteness: 序列
      • 类别标签: DEF, INDF, SPEC, NSPEC
    • Deixis: 序列
      • 类别标签: PROX, MED, REMT, REF1, REF2, NOREF, PHOR, VIS, NVIS, ABV, EVEN, BEL
    • Evidentiality: 序列
      • 类别标签: FH, DRCT, SEN, VISU, NVSEN, AUD, NFH, QUOT, RPRT, HRSY, INFER, ASSUM
    • Finiteness: 序列
      • 类别标签: FIN, NFIN
    • Gender: 序列
      • 类别标签: MASC, FEM, NEUT, NAKH1, NAKH2, NAKH3, NAKH4, NAKH5, NAKH6, NAKH7, NAKH8, BANTU1, BANTU2, BANTU3, BANTU4, BANTU5, BANTU6, BANTU7, BANTU8, BANTU9, BANTU10, BANTU11, BANTU12, BANTU13, BANTU14, BANTU15, BANTU16, BANTU17, BANTU18, BANTU19, BANTU20, BANTU21, BANTU22, BANTU23
    • Information_Structure: 序列
      • 类别标签: TOP, FOC
    • Interrogativity: 序列
      • 类别标签: DECL, INT
    • Language_Specific: 序列
      • 类别标签: LGSPEC1, LGSPEC2, LGSPEC3, LGSPEC4, LGSPEC5, LGSPEC6, LGSPEC7, LGSPEC8, LGSPEC9, LGSPEC10
    • Mood: 序列
      • 类别标签: IND, SBJV, REAL, IRR, AUPRP, AUNPRP, IMP, COND, PURP, INTEN, POT, LKLY, ADM, OBLIG, DEB, PERM, DED, SIM, OPT
    • Number: 序列
      • 类别标签: SG, PL, GRPL, DU, TRI, PAUC, GRPAUC, INVN
    • Part_Of_Speech: 序列
      • 类别标签: N, PROPN, ADJ, PRO, CLF, ART, DET, V, ADV, AUX, V.PTCP, V.MSDR, V.CVB, ADP, COMP, CONJ, NUM, PART, INTJ
    • Person: 序列
      • 类别标签: 0, 1, 2, 3, 4, INCL, EXCL, PRX, OBV
    • Polarity: 序列
      • 类别标签: POS, NEG
    • Politeness: 序列
      • 类别标签: INFM, FORM, ELEV, HUMB, POL, AVOID, LOW, HIGH, STELEV, STSUPR, LIT, FOREG, COL
    • Possession: 序列
      • 类别标签: ALN, NALN, PSS1S, PSS2S, PSS2SF, PSS2SM, PSS2SINFM, PSS2SFORM, PSS3S, PSS3SF, PSS3SM, PSS1D, PSS1DI, PSS1DE, PSS2D, PSS2DM, PSS2DF, PSS3D, PSS3DF, PSS3DM, PSS1P, PSS1PI, PSS1PE, PSS2P, PSS2PF, PSS2PM, PSS3PF, PSS3PM
    • Switch_Reference: 序列
      • 类别标签: SS, SSADV, DS, DSADV, OR, SIMMA, SEQMA, LOG
    • Tense: 序列
      • 类别标签: PRS, PST, FUT, IMMED, HOD, 1DAY, RCT, RMT
    • Valency: 序列
      • 类别标签: IMPRS, INTR, TR, DITR, REFL, RECP, CAUS, APPL
    • Voice: 序列
      • 类别标签: ACT, MID, PASS, ANTIP, DIR, INV, AGFOC, PFOC, LFOC, BFOC, ACFOC, IFOC, CFOC
    • Other: 序列
      • 字符串
• 分割:
  • train:
    • num_bytes: 6569844
    • num_examples: 1867
  • download_size: 1435972
  • dataset_size: 6569844

配置名称: ara

• 特征:
  • lemma: 字符串
  • forms: 序列
    • word: 字符串
    • Aktionsart: 序列
      • 类别标签: STAT, DYN, TEL, ATEL, PCT, DUR, ACH, ACCMP, SEMEL, ACTY
    • Animacy: 序列
      • 类别标签: ANIM, INAN, HUM, NHUM
    • Argument_Marking: 序列
      • 类别标签: ARGNO1S, ARGNO2S, ARGNO3S, ARGNO1P, ARGNO2P, ARGNO3P, ARGAC1S, ARGAC2S, ARGAC3S, ARGAC1P, ARGAC2P, ARGAC3P, ARGAB1S, ARGAB2S, ARGAB3S, ARGAB1P, ARGAB2P, ARGAB3P, ARGER1S, ARGER2S, ARGER3S, ARGER1P, ARGER2P, ARGER3P, ARGDA1S, ARGDA2S, ARGDA3S, ARGDA1P, ARGDA2P, ARGDA3P, ARGBE1S, ARGBE2S, ARGBE3S, ARGBE1P, ARGBE2P, ARGBE3P