POET: A French Extended Part-of-Speech Tagger

People Involved

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

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:

Preview Output

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

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.

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.

UD_French-GSD corpora:

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

LIA TAGG:

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

Flair Embeddings:

@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

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