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--- |
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tags: |
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- Transformers |
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- token-classification |
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- sequence-tagger-model |
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language: fr |
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datasets: |
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- qanastek/ANTILLES |
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widget: |
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- text: "George Washington est allé à Washington" |
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--- |
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# POET: A French Extended Part-of-Speech Tagger |
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- Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) |
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- Embeddings & Sequence Labelling: [CamemBERT](https://arxiv.org/abs/1911.03894) |
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- Number of Epochs: 115 |
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**People Involved** |
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* [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) |
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* [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) |
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**Affiliations** |
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1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. |
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2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. |
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## Demo: How to use in HuggingFace Transformers |
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Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers``` |
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```python |
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from transformers import CamembertTokenizer, CamembertForTokenClassification, TokenClassificationPipeline |
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tokenizer = CamembertTokenizer.from_pretrained('qanastek/pos-french-camembert') |
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model = CamembertForTokenClassification.from_pretrained('qanastek/pos-french-camembert') |
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pos = TokenClassificationPipeline(model=model, tokenizer=tokenizer) |
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def make_prediction(sentence): |
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labels = [l['entity'] for l in pos(sentence)] |
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return list(zip(sentence.split(" "), labels)) |
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res = make_prediction("George Washington est allé à Washington") |
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``` |
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Output: |
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## Training data |
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`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). |
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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. |
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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. |
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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). |
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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. |
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## Original Tags |
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```plain |
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PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ |
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``` |
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## New additional POS tags |
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| Abbreviation | Description | Examples | |
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|:--------:|:--------:|:--------:| |
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| PREP | Preposition | de | |
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| AUX | Auxiliary Verb | est | |
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| ADV | Adverb | toujours | |
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| COSUB | Subordinating conjunction | que | |
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| COCO | Coordinating Conjunction | et | |
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| PART | Demonstrative particle | -t | |
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| PRON | Pronoun | qui ce quoi | |
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| PDEMMS | Demonstrative Pronoun - Singular Masculine | ce | |
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| PDEMMP | Demonstrative Pronoun - Plural Masculine | ceux | |
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| PDEMFS | Demonstrative Pronoun - Singular Feminine | cette | |
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| PDEMFP | Demonstrative Pronoun - Plural Feminine | celles | |
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| PINDMS | Indefinite Pronoun - Singular Masculine | tout | |
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| PINDMP | Indefinite Pronoun - Plural Masculine | autres | |
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| PINDFS | Indefinite Pronoun - Singular Feminine | chacune | |
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| PINDFP | Indefinite Pronoun - Plural Feminine | certaines | |
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| PROPN | Proper noun | Houston | |
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| XFAMIL | Last name | Levy | |
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| NUM | Numerical Adjective | trentaine vingtaine | |
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| DINTMS | Masculine Numerical Adjective | un | |
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| DINTFS | Feminine Numerical Adjective | une | |
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| PPOBJMS | Pronoun complements of objects - Singular Masculine | le lui | |
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| PPOBJMP | Pronoun complements of objects - Plural Masculine | eux y | |
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| PPOBJFS | Pronoun complements of objects - Singular Feminine | moi la | |
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| PPOBJFP | Pronoun complements of objects - Plural Feminine | en y | |
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| PPER1S | Personal Pronoun First-Person - Singular | je | |
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| PPER2S | Personal Pronoun Second-Person - Singular | tu | |
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| PPER3MS | Personal Pronoun Third-Person - Singular Masculine | il | |
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| PPER3MP | Personal Pronoun Third-Person - Plural Masculine | ils | |
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| PPER3FS | Personal Pronoun Third-Person - Singular Feminine | elle | |
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| PPER3FP | Personal Pronoun Third-Person - Plural Feminine | elles | |
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| PREFS | Reflexive Pronoun First-Person - Singular | me m' | |
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| PREF | Reflexive Pronoun Third-Person - Singular | se s' | |
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| PREFP | Reflexive Pronoun First / Second-Person - Plural | nous vous | |
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| VERB | Verb | obtient | |
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| VPPMS | Past Participle - Singular Masculine | formulé | |
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| VPPMP | Past Participle - Plural Masculine | classés | |
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| VPPFS | Past Participle - Singular Feminine | appelée | |
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| VPPFP | Past Participle - Plural Feminine | sanctionnées | |
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| DET | Determinant | les l' | |
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| DETMS | Determinant - Singular Masculine | les | |
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| DETFS | Determinant - Singular Feminine | la | |
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| ADJ | Adjective | capable sérieux | |
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| ADJMS | Adjective - Singular Masculine | grand important | |
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| ADJMP | Adjective - Plural Masculine | grands petits | |
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| ADJFS | Adjective - Singular Feminine | française petite | |
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| ADJFP | Adjective - Plural Feminine | légères petites | |
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| NOUN | Noun | temps | |
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| NMS | Noun - Singular Masculine | drapeau | |
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| NMP | Noun - Plural Masculine | journalistes | |
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| NFS | Noun - Singular Feminine | tête | |
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| NFP | Noun - Plural Feminine | ondes | |
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| PREL | Relative Pronoun | qui dont | |
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| PRELMS | Relative Pronoun - Singular Masculine | lequel | |
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| PRELMP | Relative Pronoun - Plural Masculine | lesquels | |
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| PRELFS | Relative Pronoun - Singular Feminine | laquelle | |
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| PRELFP | Relative Pronoun - Plural Feminine | lesquelles | |
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| INTJ | Interjection | merci bref | |
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| CHIF | Numbers | 1979 10 | |
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| SYM | Symbol | € % | |
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| YPFOR | Endpoint | . | |
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| PUNCT | Ponctuation | : , | |
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| MOTINC | Unknown words | Technology Lady | |
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| X | Typos & others | sfeir 3D statu | |
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## Evaluation results |
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The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). |
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```plain |
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precision recall f1-score support |
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ADJ 0.9040 0.8828 0.8933 128 |
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ADJFP 0.9811 0.9585 0.9697 434 |
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ADJFS 0.9606 0.9826 0.9715 918 |
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ADJMP 0.9613 0.9357 0.9483 451 |
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ADJMS 0.9561 0.9611 0.9586 952 |
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ADV 0.9870 0.9948 0.9908 1524 |
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AUX 0.9956 0.9964 0.9960 1124 |
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CHIF 0.9798 0.9774 0.9786 1239 |
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COCO 1.0000 0.9989 0.9994 884 |
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COSUB 0.9939 0.9939 0.9939 328 |
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DET 0.9972 0.9972 0.9972 2897 |
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DETFS 0.9990 1.0000 0.9995 1007 |
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DETMS 1.0000 0.9993 0.9996 1426 |
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DINTFS 0.9967 0.9902 0.9934 306 |
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DINTMS 0.9923 0.9948 0.9935 387 |
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INTJ 0.8000 0.8000 0.8000 5 |
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MOTINC 0.5049 0.5827 0.5410 266 |
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NFP 0.9807 0.9675 0.9740 892 |
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NFS 0.9778 0.9699 0.9738 2588 |
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NMP 0.9687 0.9495 0.9590 1367 |
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NMS 0.9759 0.9560 0.9659 3181 |
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NOUN 0.6164 0.8673 0.7206 113 |
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NUM 0.6250 0.8333 0.7143 6 |
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PART 1.0000 0.9375 0.9677 16 |
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PDEMFP 1.0000 1.0000 1.0000 3 |
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PDEMFS 1.0000 1.0000 1.0000 89 |
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PDEMMP 1.0000 1.0000 1.0000 20 |
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PDEMMS 1.0000 1.0000 1.0000 222 |
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PINDFP 1.0000 1.0000 1.0000 3 |
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PINDFS 0.8571 1.0000 0.9231 12 |
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PINDMP 0.9000 1.0000 0.9474 9 |
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PINDMS 0.9286 0.9701 0.9489 67 |
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PINTFS 0.0000 0.0000 0.0000 2 |
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PPER1S 1.0000 1.0000 1.0000 62 |
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PPER2S 0.7500 1.0000 0.8571 3 |
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PPER3FP 1.0000 1.0000 1.0000 9 |
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PPER3FS 1.0000 1.0000 1.0000 96 |
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PPER3MP 1.0000 1.0000 1.0000 31 |
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PPER3MS 1.0000 1.0000 1.0000 377 |
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PPOBJFP 1.0000 0.7500 0.8571 4 |
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PPOBJFS 0.9167 0.8919 0.9041 37 |
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PPOBJMP 0.7500 0.7500 0.7500 12 |
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PPOBJMS 0.9371 0.9640 0.9504 139 |
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PREF 1.0000 1.0000 1.0000 332 |
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PREFP 1.0000 1.0000 1.0000 64 |
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PREFS 1.0000 1.0000 1.0000 13 |
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PREL 0.9964 0.9964 0.9964 277 |
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PRELFP 1.0000 1.0000 1.0000 5 |
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PRELFS 0.8000 1.0000 0.8889 4 |
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PRELMP 1.0000 1.0000 1.0000 3 |
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PRELMS 1.0000 1.0000 1.0000 11 |
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PREP 0.9971 0.9977 0.9974 6161 |
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PRON 0.9836 0.9836 0.9836 61 |
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PROPN 0.9468 0.9503 0.9486 4310 |
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PUNCT 1.0000 1.0000 1.0000 4019 |
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SYM 0.9394 0.8158 0.8732 76 |
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VERB 0.9956 0.9921 0.9938 2273 |
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VPPFP 0.9145 0.9469 0.9304 113 |
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VPPFS 0.9562 0.9597 0.9580 273 |
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VPPMP 0.8827 0.9728 0.9256 147 |
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VPPMS 0.9778 0.9794 0.9786 630 |
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VPPRE 0.0000 0.0000 0.0000 1 |
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X 0.9604 0.9935 0.9766 1073 |
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XFAMIL 0.9386 0.9113 0.9248 1342 |
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YPFOR 1.0000 1.0000 1.0000 2750 |
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accuracy 0.9778 47574 |
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macro avg 0.9151 0.9285 0.9202 47574 |
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weighted avg 0.9785 0.9778 0.9780 47574 |
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``` |
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## BibTeX Citations |
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Please cite the following paper when using this model. |
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ANTILLES corpus and POET taggers: |
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```latex |
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@inproceedings{labrak:hal-03696042, |
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TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, |
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AUTHOR = {Labrak, Yanis and Dufour, Richard}, |
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URL = {https://hal.archives-ouvertes.fr/hal-03696042}, |
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BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, |
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ADDRESS = {Brno, Czech Republic}, |
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PUBLISHER = {{Springer}}, |
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YEAR = {2022}, |
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MONTH = Sep, |
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KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, |
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PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, |
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HAL_ID = {hal-03696042}, |
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HAL_VERSION = {v1}, |
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} |
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``` |
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UD_French-GSD corpora: |
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```latex |
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@misc{ |
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universaldependencies, |
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title={UniversalDependencies/UD_French-GSD}, |
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url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, |
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author={UniversalDependencies} |
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} |
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``` |
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LIA TAGG: |
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```latex |
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@techreport{LIA_TAGG, |
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author = {Frédéric Béchet}, |
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title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, |
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institution = {Aix-Marseille University & CNRS}, |
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year = {2001} |
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} |
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``` |
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Flair Embeddings: |
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```latex |
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@inproceedings{akbik2018coling, |
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title={Contextual String Embeddings for Sequence Labeling}, |
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author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, |
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booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, |
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pages = {1638--1649}, |
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year = {2018} |
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} |
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``` |
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## Acknowledgment |
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This work was financially supported by [Zenidoc](https://zenidoc.fr/) |
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