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--- |
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tags: |
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- flair |
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- token-classification |
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- sequence-tagger-model |
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language: fr |
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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: [FastText](https://fasttext.cc/) |
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- Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088) |
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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 Flair |
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Requires [Flair](https://pypi.org/project/flair/): ```pip install flair``` |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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# Load the model |
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model = SequenceTagger.load("qanastek/pos-french") |
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sentence = Sentence("George Washington est allé à Washington") |
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# predict tags |
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model.predict(sentence) |
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# print predicted pos tags |
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print(sentence.to_tagged_string()) |
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``` |
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Output: |
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 |
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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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Results: |
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- F-score (micro): 0.952 |
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- F-score (macro): 0.8644 |
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- Accuracy (incl. no class): 0.952 |
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By class: |
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precision recall f1-score support |
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PPER1S 0.9767 1.0000 0.9882 42 |
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VERB 0.9823 0.9537 0.9678 583 |
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COSUB 0.9344 0.8906 0.9120 128 |
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PUNCT 0.9878 0.9688 0.9782 833 |
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PREP 0.9767 0.9879 0.9822 1483 |
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PDEMMS 0.9583 0.9200 0.9388 75 |
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COCO 0.9839 1.0000 0.9919 245 |
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DET 0.9679 0.9814 0.9746 645 |
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NMP 0.9521 0.9115 0.9313 305 |
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ADJMP 0.8352 0.9268 0.8786 82 |
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PREL 0.9324 0.9857 0.9583 70 |
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PREFP 0.9767 0.9545 0.9655 44 |
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AUX 0.9537 0.9859 0.9695 355 |
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ADV 0.9440 0.9365 0.9402 504 |
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VPPMP 0.8667 1.0000 0.9286 26 |
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DINTMS 0.9919 1.0000 0.9959 122 |
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ADJMS 0.9020 0.9057 0.9039 244 |
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NMS 0.9226 0.9336 0.9281 753 |
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NFS 0.9347 0.9714 0.9527 560 |
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YPFOR 0.9806 1.0000 0.9902 353 |
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PINDMS 1.0000 0.9091 0.9524 44 |
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NOUN 0.8400 0.5385 0.6562 39 |
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PROPN 0.8605 0.8278 0.8439 395 |
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DETMS 0.9972 0.9972 0.9972 362 |
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PPER3MS 0.9341 0.9770 0.9551 87 |
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VPPMS 0.8994 0.9682 0.9325 157 |
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DETFS 1.0000 1.0000 1.0000 240 |
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ADJFS 0.9266 0.9011 0.9136 182 |
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ADJFP 0.9726 0.9342 0.9530 76 |
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NFP 0.9463 0.9749 0.9604 199 |
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VPPFS 0.8000 0.9000 0.8471 40 |
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CHIF 0.9543 0.9414 0.9478 222 |
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XFAMIL 0.9346 0.8696 0.9009 115 |
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PPER3MP 0.9474 0.9000 0.9231 20 |
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PPOBJMS 0.8800 0.9362 0.9072 47 |
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PREF 0.8889 0.9231 0.9057 52 |
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PPOBJMP 1.0000 0.6000 0.7500 10 |
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SYM 0.9706 0.8684 0.9167 38 |
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DINTFS 0.9683 1.0000 0.9839 61 |
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PDEMFS 1.0000 0.8966 0.9455 29 |
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PPER3FS 1.0000 0.9444 0.9714 18 |
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VPPFP 0.9500 1.0000 0.9744 19 |
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PRON 0.9200 0.7419 0.8214 31 |
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PPOBJFS 0.8333 0.8333 0.8333 6 |
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PART 0.8000 1.0000 0.8889 4 |
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PPER3FP 1.0000 1.0000 1.0000 2 |
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MOTINC 0.3571 0.3333 0.3448 15 |
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PDEMMP 1.0000 0.6667 0.8000 3 |
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INTJ 0.4000 0.6667 0.5000 6 |
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PREFS 1.0000 0.5000 0.6667 10 |
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ADJ 0.7917 0.8636 0.8261 22 |
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PINDMP 0.0000 0.0000 0.0000 1 |
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PINDFS 1.0000 1.0000 1.0000 1 |
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NUM 1.0000 0.3333 0.5000 3 |
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PPER2S 1.0000 1.0000 1.0000 2 |
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PPOBJFP 1.0000 0.5000 0.6667 2 |
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PDEMFP 1.0000 0.6667 0.8000 3 |
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X 0.0000 0.0000 0.0000 1 |
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PRELMS 1.0000 1.0000 1.0000 2 |
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PINDFP 1.0000 1.0000 1.0000 1 |
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accuracy 0.9520 10019 |
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macro avg 0.8956 0.8521 0.8644 10019 |
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weighted avg 0.9524 0.9520 0.9515 10019 |
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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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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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