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ANTILLES

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Token Classification
Languages:
French
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ANTILLES : An Open French Linguistically Enriched Part-of-Speech Corpus

Table of Contents

Dataset Description

Dataset Summary

ANTILLES is a part-of-speech tagging corpora based on UD_French-GSD which was originally created in 2015 and is based on the universal dependency treebank v2.0.

Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation script transform.py, we obtain 60 different classes which add semantic information such as: the gender, number, mood, person, tense or verb form given in the different CoNLL-U fields from the original corpora.

We based our tags on the level of details given by the LIA_TAGG statistical POS tagger written by Frédéric Béchet in 2001.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Supported Tasks and Leaderboards

part-of-speech-tagging: The dataset can be used to train a model for part-of-speech-tagging. The performance is measured by how high its F1 score is. A Flair Sequence-To-Sequence model trained to tag tokens from Wikipedia passages achieves a F1 score (micro) of 0.952.

Languages

The text in the dataset is in French, as spoken by Wikipedia users. The associated BCP-47 code is fr.

Load the dataset

HuggingFace 

from datasets import load_dataset
dataset = load_dataset("qanastek/ANTILLES")
print(dataset)

FlairNLP 

from flair.datasets import UniversalDependenciesCorpus
corpus: Corpus = UniversalDependenciesCorpus(
    data_folder='ANTILLES',
    train_file="train.conllu",
    test_file="test.conllu",
    dev_file="dev.conllu"
)

Load the model

Flair 

from flair.models import SequenceTagger
tagger = SequenceTagger.load("qanastek/pos-french")

HuggingFace Spaces

You can try some of the models :

Dataset Structure

Data Instances 

# sent_id = fr-ud-dev_00005
# text = Travail de trés grande qualité exécuté par un imprimeur artisan passionné.
1	Travail	travail	NMS	_	Gender=Masc|Number=Sing	0	root	_	wordform=travail
2	de	de	PREP	_	_	5	case	_	_
3	trés	trés	ADV	_	_	4	advmod	_	_
4	grande	grand	ADJFS	_	Gender=Fem|Number=Sing	5	amod	_	_
5	qualité	qualité	NFS	_	Gender=Fem|Number=Sing	1	nmod	_	_
6	exécuté	exécuter	VPPMS	_	Gender=Masc|Number=Sing|Tense=Past|VerbForm=Part	1	acl	_	_
7	par	par	PREP	_	_	9	case	_	_
8	un	un	DINTMS	_	Definite=Ind|Gender=Masc|Number=Sing|PronType=Art	9	det	_	_
9	imprimeur	imprimeur	NMS	_	Gender=Masc|Number=Sing	6	obl:agent	_	_
10	artisan	artisan	NMS	_	Gender=Masc|Number=Sing	9	nmod	_	_
11	passionné	passionné	ADJMS	_	Gender=Masc|Number=Sing	9	amod	_	SpaceAfter=No
12	.	.	YPFOR	_	_	1	punct	_	_

Data Fields

Abbreviation Description Examples # tokens
PREP Preposition de 63 738
AUX Auxiliary Verb est 12 886
ADV Adverb toujours 14 969
COSUB Subordinating conjunction que 3 007
COCO Coordinating Conjunction et 10 102
PART Demonstrative particle -t 93
PRON Pronoun qui ce quoi 667
PDEMMS Singular Masculine Demonstrative Pronoun ce 1 950
PDEMMP Plurial Masculine Demonstrative Pronoun ceux 108
PDEMFS Singular Feminine Demonstrative Pronoun cette 1 004
PDEMFP Plurial Feminine Demonstrative Pronoun celles 53
PINDMS Singular Masculine Indefinite Pronoun tout 961
PINDMP Plurial Masculine Indefinite Pronoun autres 89
PINDFS Singular Feminine Indefinite Pronoun chacune 136
PINDFP Plurial Feminine Indefinite Pronoun certaines 31
PROPN Proper noun houston 22 135
XFAMIL Last name levy 6 449
NUM Numerical Adjectives trentaine vingtaine 67
DINTMS Masculine Numerical Adjectives un 4 254
DINTFS Feminine Numerical Adjectives une 3 543
PPOBJMS Singular Masculine Pronoun complements of objects le lui 1 425
PPOBJMP Plurial Masculine Pronoun complements of objects eux y 212
PPOBJFS Singular Feminine Pronoun complements of objects moi la 358
PPOBJFP Plurial Feminine Pronoun complements of objects en y 70
PPER1S Personal Pronoun First Person Singular je 571
PPER2S Personal Pronoun Second Person Singular tu 19
PPER3MS Personal Pronoun Third Person Masculine Singular il 3 938
PPER3MP Personal Pronoun Third Person Masculine Plurial ils 513
PPER3FS Personal Pronoun Third Person Feminine Singular elle 992
PPER3FP Personal Pronoun Third Person Feminine Plurial elles 121
PREFS Reflexive Pronouns First Person of Singular me m' 120
PREF Reflexive Pronouns Third Person of Singular se s' 2 337
PREFP Reflexive Pronouns First / Second Person of Plurial nous vous 686
VERB Verb obtient 21 131
VPPMS Singular Masculine Participle Past Verb formulé 6 275
VPPMP Plurial Masculine Participle Past Verb classés 1 352
VPPFS Singular Feminine Participle Past Verb appelée 2 434
VPPFP Plurial Feminine Participle Past Verb sanctionnées 813
VPPRE Present participle étant 2
DET Determinant les l' 25 206
DETMS Singular Masculine Determinant les 15 444
DETFS Singular Feminine Determinant la 10 978
ADJ Adjective capable sérieux 1 075
ADJMS Singular Masculine Adjective grand important 8 338
ADJMP Plurial Masculine Adjective grands petits 3 274
ADJFS Singular Feminine Adjective franéaise petite 8 004
ADJFP Plurial Feminine Adjective légéres petites 3 041
NOUN Noun temps 1 389
NMS Singular Masculine Noun drapeau 29 698
NMP Plurial Masculine Noun journalistes 10 882
NFS Singular Feminine Noun téte 25 414
NFP Plurial Feminine Noun ondes 7 448
PREL Relative Pronoun qui dont 2 976
PRELMS Singular Masculine Relative Pronoun lequel 94
PRELMP Plurial Masculine Relative Pronoun lesquels 29
PRELFS Singular Feminine Relative Pronoun laquelle 70
PRELFP Plurial Feminine Relative Pronoun lesquelles 25
PINTFS Singular Feminine Interrogative Pronoun laquelle 3
INTJ Interjection merci bref 75
CHIF Numbers 1979 10 10 417
SYM Symbol é % 705
YPFOR Endpoint . 15 088
PUNCT Ponctuation : , 28 918
MOTINC Unknown words Technology Lady 2 022
X Typos & others sfeir 3D statu 175

Data Splits

Train Dev Test
# Docs 14 449 1 476 416
Avg # Tokens / Doc 24.54 24.19 24.08

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

The corpora is free of personal or sensitive information since it has been based on Wikipedia articles content.

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

The nature of the corpora introduce various biases such as the names of the streets which are temporaly based and can therefore introduce named entity like author or event names. For example, street names such as Rue Victor-Hugo or Rue Pasteur doesn't exist before the 20's century in France.

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

ANTILLES: Labrak Yanis, Dufour Richard

UD_FRENCH-GSD: de Marneffe Marie-Catherine, Guillaume Bruno, McDonald Ryan, Suhr Alane, Nivre Joakim, Grioni Matias, Dickerson Carly, Perrier Guy

Universal Dependency: Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee

Licensing Information 

For the following languages

  German, Spanish, French, Indonesian, Italian, Japanese, Korean and Brazilian
  Portuguese

we will distinguish between two portions of the data.

1. The underlying text for sentences that were annotated. This data Google
   asserts no ownership over and no copyright over. Some or all of these
   sentences may be copyrighted in some jurisdictions.  Where copyrighted,
   Google collected these sentences under exceptions to copyright or implied
   license rights.  GOOGLE MAKES THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY
   WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED.

2. The annotations -- part-of-speech tags and dependency annotations. These are
   made available under a CC BY-SA 4.0. GOOGLE MAKES
   THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY WARRANTY OF ANY KIND, WHETHER
   EXPRESS OR IMPLIED. See attached LICENSE file for the text of CC BY-NC-SA.

Portions of the German data were sampled from the CoNLL 2006 Tiger Treebank
data. Hans Uszkoreit graciously gave permission to use the underlying
sentences in this data as part of this release.

Any use of the data should reference the above plus:

  Universal Dependency Annotation for Multilingual Parsing
  Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg,
  Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang,
  Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee
  Proceedings of ACL 2013

Citation Information

Please cite the following paper when using this model.

UD_French-GSD corpora:

@misc{
    universaldependencies,
    title={UniversalDependencies/UD_French-GSD},
    url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub},
    author={UniversalDependencies}
}

{U}niversal {D}ependency Annotation for Multilingual Parsing:

@inproceedings{mcdonald-etal-2013-universal,
    title = "{U}niversal {D}ependency Annotation for Multilingual Parsing",
    author = {McDonald, Ryan  and
      Nivre, Joakim  and
      Quirmbach-Brundage, Yvonne  and
      Goldberg, Yoav  and
      Das, Dipanjan  and
      Ganchev, Kuzman  and
      Hall, Keith  and
      Petrov, Slav  and
      Zhang, Hao  and
      T{\"a}ckstr{\"o}m, Oscar  and
      Bedini, Claudia  and
      Bertomeu Castell{\'o}, N{\'u}ria  and
      Lee, Jungmee},
    booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2013",
    address = "Sofia, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P13-2017",
    pages = "92--97",
}

LIA TAGG:

@techreport{LIA_TAGG,
  author = {Frédéric Béchet},
  title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer},
  institution = {Aix-Marseille University & CNRS},
  year = {2001}
}