POET: A French Extended Part-of-Speech Tagger
- Corpora: ANTILLES
- Embeddings: Flair & CamemBERT
- Sequence Labelling: Bi-LSTM-CRF
- Number of Epochs: 50
People Involved
- LABRAK Yanis (1)
- DUFOUR Richard (2)
Affiliations
- LIA, NLP team, Avignon University, Avignon, France.
- 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:
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) 0.9797
- F-score (macro) 0.9178
- Accuracy 0.9797
By class:
precision recall f1-score support
PREP 0.9966 0.9987 0.9976 1483
PUNCT 1.0000 1.0000 1.0000 833
NMS 0.9634 0.9801 0.9717 753
DET 0.9923 0.9984 0.9954 645
VERB 0.9913 0.9811 0.9862 583
NFS 0.9667 0.9839 0.9752 560
ADV 0.9940 0.9821 0.9880 504
PROPN 0.9541 0.8937 0.9229 395
DETMS 1.0000 1.0000 1.0000 362
AUX 0.9860 0.9915 0.9888 355
YPFOR 1.0000 1.0000 1.0000 353
NMP 0.9666 0.9475 0.9570 305
COCO 0.9959 1.0000 0.9980 245
ADJMS 0.9463 0.9385 0.9424 244
DETFS 1.0000 1.0000 1.0000 240
CHIF 0.9648 0.9865 0.9755 222
NFP 0.9515 0.9849 0.9679 199
ADJFS 0.9657 0.9286 0.9468 182
VPPMS 0.9387 0.9745 0.9563 157
COSUB 1.0000 0.9844 0.9921 128
DINTMS 0.9918 0.9918 0.9918 122
XFAMIL 0.9298 0.9217 0.9258 115
PPER3MS 1.0000 1.0000 1.0000 87
ADJMP 0.9294 0.9634 0.9461 82
PDEMMS 1.0000 1.0000 1.0000 75
ADJFP 0.9861 0.9342 0.9595 76
PREL 0.9859 1.0000 0.9929 70
DINTFS 0.9839 1.0000 0.9919 61
PREF 1.0000 1.0000 1.0000 52
PPOBJMS 0.9565 0.9362 0.9462 47
PREFP 0.9778 1.0000 0.9888 44
PINDMS 1.0000 0.9773 0.9885 44
VPPFS 0.8298 0.9750 0.8966 40
PPER1S 1.0000 1.0000 1.0000 42
SYM 1.0000 0.9474 0.9730 38
NOUN 0.8824 0.7692 0.8219 39
PRON 1.0000 0.9677 0.9836 31
PDEMFS 1.0000 1.0000 1.0000 29
VPPMP 0.9286 1.0000 0.9630 26
ADJ 0.9524 0.9091 0.9302 22
PPER3MP 1.0000 1.0000 1.0000 20
VPPFP 1.0000 1.0000 1.0000 19
PPER3FS 1.0000 1.0000 1.0000 18
MOTINC 0.3333 0.4000 0.3636 15
PREFS 1.0000 1.0000 1.0000 10
PPOBJMP 1.0000 0.8000 0.8889 10
PPOBJFS 0.6250 0.8333 0.7143 6
INTJ 0.5000 0.6667 0.5714 6
PART 1.0000 1.0000 1.0000 4
PDEMMP 1.0000 1.0000 1.0000 3
PDEMFP 1.0000 1.0000 1.0000 3
PPER3FP 1.0000 1.0000 1.0000 2
NUM 1.0000 0.3333 0.5000 3
PPER2S 1.0000 1.0000 1.0000 2
PPOBJFP 0.5000 0.5000 0.5000 2
PRELMS 1.0000 1.0000 1.0000 2
PINDFS 0.5000 1.0000 0.6667 1
PINDMP 1.0000 1.0000 1.0000 1
X 0.0000 0.0000 0.0000 1
PINDFP 1.0000 1.0000 1.0000 1
micro avg 0.9797 0.9797 0.9797 10019
macro avg 0.9228 0.9230 0.9178 10019
weighted avg 0.9802 0.9797 0.9798 10019
samples avg 0.9797 0.9797 0.9797 10019
BibTeX Citations
Please cite the following paper when using this model.
ANTILLES corpus and POET taggers:
@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:
@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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