--- tags: - flair - token-classification - sequence-tagger-model language: fr datasets: - qanastek/ANTILLES widget: - text: "George Washington est allé à Washington" - text: "George Washington est allé à Washington" --- # POET: A French Extended Part-of-Speech Tagger - Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) - Embeddings: [FastText](https://fasttext.cc/) - Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088) - Number of Epochs: 115 **People Involved** * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) * [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) **Affiliations** 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. 2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. ## Demo: How to use in Flair Requires [Flair](https://pypi.org/project/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: ![Preview Output](preview.PNG) ## Training data `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). 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](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html). 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](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). ```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](https://zenidoc.fr/)