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qanastek/pos-french

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Model Card

POET: A French Extended Part-of-Speech Tagger

—Corpora: ANTILLES
—Embeddings: FastText
—Sequence Labelling: Bi-LSTM-CRF
—Number of Epochs: 115

People Involved

—LABRAK Yanis (1)
—DUFOUR Richard (2)

Affiliations

1.LIA, NLP team, Avignon University, Avignon, France.
2.LS2N, TALN team, Nantes University, Nantes, France.

Demo: How to use in Flair

Requires Flair: ``pip install flair``

python

from flair.data import Sentence
from flair.models import SequenceTagger

# Load the model
model = SequenceTagger.load("qanastek/pos-french")

sentence = Sentence("George Washington est allé à Washington")

# Predict tags
model.predict(sentence)

# Print predicted pos tags
print(sentence.to_tagged_string())

Output:

[image]

Training data

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 we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 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.

The corpora used for this model is available on Github at the CoNLL-U format.

Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive.

Original Tags

plain

PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ

New additional POS tags

Abbreviation	Description	Examples
PREP	Preposition	de
AUX	Auxiliary Verb	est
ADV	Adverb	toujours
COSUB	Subordinating conjunction	que
COCO	Coordinating Conjunction	et
PART	Demonstrative particle	-t
PRON	Pronoun	qui ce quoi
PDEMMS	Demonstrative Pronoun - Singular Masculine	ce
PDEMMP	Demonstrative Pronoun - Plural Masculine	ceux
PDEMFS	Demonstrative Pronoun - Singular Feminine	cette
PDEMFP	Demonstrative Pronoun - Plural Feminine	celles
PINDMS	Indefinite Pronoun - Singular Masculine	tout
PINDMP	Indefinite Pronoun - Plural Masculine	autres
PINDFS	Indefinite Pronoun - Singular Feminine	chacune
PINDFP	Indefinite Pronoun - Plural Feminine	certaines
PROPN	Proper noun	Houston
XFAMIL	Last name	Levy
NUM	Numerical Adjective	trentaine vingtaine
DINTMS	Masculine Numerical Adjective	un
DINTFS	Feminine Numerical Adjective	une
PPOBJMS	Pronoun complements of objects - Singular Masculine	le lui
PPOBJMP	Pronoun complements of objects - Plural Masculine	eux y
PPOBJFS	Pronoun complements of objects - Singular Feminine	moi la
PPOBJFP	Pronoun complements of objects - Plural Feminine	en y
PPER1S	Personal Pronoun First-Person - Singular	je
PPER2S	Personal Pronoun Second-Person - Singular	tu
PPER3MS	Personal Pronoun Third-Person - Singular Masculine	il
PPER3MP	Personal Pronoun Third-Person - Plural Masculine	ils
PPER3FS	Personal Pronoun Third-Person - Singular Feminine	elle
PPER3FP	Personal Pronoun Third-Person - Plural Feminine	elles
PREFS	Reflexive Pronoun First-Person - Singular	me m'
PREF	Reflexive Pronoun Third-Person - Singular	se s'
PREFP	Reflexive Pronoun First / Second-Person - Plural	nous vous
VERB	Verb	obtient
VPPMS	Past Participle - Singular Masculine	formulé
VPPMP	Past Participle - Plural Masculine	classés
VPPFS	Past Participle - Singular Feminine	appelée
VPPFP	Past Participle - Plural Feminine	sanctionnées
DET	Determinant	les l'
DETMS	Determinant - Singular Masculine	les
DETFS	Determinant - Singular Feminine	la
ADJ	Adjective	capable sérieux
ADJMS	Adjective - Singular Masculine	grand important
ADJMP	Adjective - Plural Masculine	grands petits
ADJFS	Adjective - Singular Feminine	française petite
ADJFP	Adjective - Plural Feminine	légères petites
NOUN	Noun	temps
NMS	Noun - Singular Masculine	drapeau
NMP	Noun - Plural Masculine	journalistes
NFS	Noun - Singular Feminine	tête
NFP	Noun - Plural Feminine	ondes
PREL	Relative Pronoun	qui dont
PRELMS	Relative Pronoun - Singular Masculine	lequel
PRELMP	Relative Pronoun - Plural Masculine	lesquels
PRELFS	Relative Pronoun - Singular Feminine	laquelle
PRELFP	Relative Pronoun - Plural Feminine	lesquelles
INTJ	Interjection	merci bref
CHIF	Numbers	1979 10
SYM	Symbol	€ %
YPFOR	Endpoint	.
PUNCT	Ponctuation	: ,
MOTINC	Unknown words	Technology Lady
X	Typos & others	sfeir 3D statu

Evaluation results

The test corpora used for this evaluation is available on Github.

plain

Results:
- F-score (micro): 0.952
- F-score (macro): 0.8644
- Accuracy (incl. no class): 0.952

By class:
              precision    recall  f1-score   support
      PPER1S     0.9767    1.0000    0.9882        42
        VERB     0.9823    0.9537    0.9678       583
       COSUB     0.9344    0.8906    0.9120       128
       PUNCT     0.9878    0.9688    0.9782       833
        PREP     0.9767    0.9879    0.9822      1483
      PDEMMS     0.9583    0.9200    0.9388        75
        COCO     0.9839    1.0000    0.9919       245
         DET     0.9679    0.9814    0.9746       645
         NMP     0.9521    0.9115    0.9313       305
       ADJMP     0.8352    0.9268    0.8786        82
        PREL     0.9324    0.9857    0.9583        70
       PREFP     0.9767    0.9545    0.9655        44
         AUX     0.9537    0.9859    0.9695       355
         ADV     0.9440    0.9365    0.9402       504
       VPPMP     0.8667    1.0000    0.9286        26
      DINTMS     0.9919    1.0000    0.9959       122
       ADJMS     0.9020    0.9057    0.9039       244
         NMS     0.9226    0.9336    0.9281       753
         NFS     0.9347    0.9714    0.9527       560
       YPFOR     0.9806    1.0000    0.9902       353
      PINDMS     1.0000    0.9091    0.9524        44
        NOUN     0.8400    0.5385    0.6562        39
       PROPN     0.8605    0.8278    0.8439       395
       DETMS     0.9972    0.9972    0.9972       362
     PPER3MS     0.9341    0.9770    0.9551        87
       VPPMS     0.8994    0.9682    0.9325       157
       DETFS     1.0000    1.0000    1.0000       240
       ADJFS     0.9266    0.9011    0.9136       182
       ADJFP     0.9726    0.9342    0.9530        76
         NFP     0.9463    0.9749    0.9604       199
       VPPFS     0.8000    0.9000    0.8471        40
        CHIF     0.9543    0.9414    0.9478       222
      XFAMIL     0.9346    0.8696    0.9009       115
     PPER3MP     0.9474    0.9000    0.9231        20
     PPOBJMS     0.8800    0.9362    0.9072        47
        PREF     0.8889    0.9231    0.9057        52
     PPOBJMP     1.0000    0.6000    0.7500        10
         SYM     0.9706    0.8684    0.9167        38
      DINTFS     0.9683    1.0000    0.9839        61
      PDEMFS     1.0000    0.8966    0.9455        29
     PPER3FS     1.0000    0.9444    0.9714        18
       VPPFP     0.9500    1.0000    0.9744        19
        PRON     0.9200    0.7419    0.8214        31
     PPOBJFS     0.8333    0.8333    0.8333         6
        PART     0.8000    1.0000    0.8889         4
     PPER3FP     1.0000    1.0000    1.0000         2
      MOTINC     0.3571    0.3333    0.3448        15
      PDEMMP     1.0000    0.6667    0.8000         3
        INTJ     0.4000    0.6667    0.5000         6
       PREFS     1.0000    0.5000    0.6667        10
         ADJ     0.7917    0.8636    0.8261        22
      PINDMP     0.0000    0.0000    0.0000         1
      PINDFS     1.0000    1.0000    1.0000         1
         NUM     1.0000    0.3333    0.5000         3
      PPER2S     1.0000    1.0000    1.0000         2
     PPOBJFP     1.0000    0.5000    0.6667         2
      PDEMFP     1.0000    0.6667    0.8000         3
           X     0.0000    0.0000    0.0000         1
      PRELMS     1.0000    1.0000    1.0000         2
      PINDFP     1.0000    1.0000    1.0000         1

    accuracy                         0.9520     10019
   macro avg     0.8956    0.8521    0.8644     10019
weighted avg     0.9524    0.9520    0.9515     10019

BibTeX Citations

Please cite the following paper when using this model.

ANTILLES corpus and POET taggers:

latex

@inproceedings{labrak:hal-03696042,
  TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}},
  AUTHOR = {Labrak, Yanis and Dufour, Richard},
  URL = {https://hal.archives-ouvertes.fr/hal-03696042},
  BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}},
  ADDRESS = {Brno, Czech Republic},
  PUBLISHER = {{Springer}},
  YEAR = {2022},
  MONTH = Sep,
  KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers},
  PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf},
  HAL_ID = {hal-03696042},
  HAL_VERSION = {v1},
}

UD_French-GSD corpora:

latex

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

LIA TAGG:

latex

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

Flair Embeddings:

latex

@inproceedings{akbik2018coling,
  title={Contextual String Embeddings for Sequence Labeling},
  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
  pages     = {1638--1649},
  year      = {2018}
}

Acknowledgment

This work was financially supported by Zenidoc