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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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**People Involved** |
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* LABRAK Yanis (1) |
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* DUFOUR Richard (2) |
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**Affiliations** |
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1. LIA, Avignon University, Avignon, France. |
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2. LS2N, Nantes University, Nantes, France. |
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# POET: A French Extended Part-of-Speech Tagger |
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- Corpus: [UD_FRENCH_TREEBANKS](https://universaldependencies.org/treebanks/fr_gsd/index.html) |
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- Model: [Flair](https://www.aclweb.org/anthology/C18-1139.pdf) |
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- Embeddings: [FastText](https://fasttext.cc/) |
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- Additionnel: [LSTM-CRF](https://arxiv.org/abs/1011.4088) |
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- Nombre d'Epochs: 115 |
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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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## Training data |
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`UD_FRENCH_GSD_Plus` 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 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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## 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 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 | Singular Masculine Demonstrative Pronoun | ce | |
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| PDEMMP | Plurial Masculine Demonstrative Pronoun | ceux | |
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| PDEMFS | Singular Feminine Demonstrative Pronoun | cette | |
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| PDEMFP | Plurial Feminine Demonstrative Pronoun | celles | |
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| PINDMS | Singular Masculine Indefinite Pronoun | tout | |
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| PINDMP | Plurial Masculine Indefinite Pronoun | autres | |
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| PINDFS | Singular Feminine Indefinite Pronoun | chacune | |
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| PINDFP | Plurial Feminine Indefinite Pronoun | certaines | |
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| PROPN | Proper noun | houston | |
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| XFAMIL | Last name | levy | |
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| NUM | Numerical Adjectives | trentaine vingtaine | |
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| DINTMS | Masculine Numerical Adjectives | un | |
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| DINTFS | Feminine Numerical Adjectives | une | |
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| PPOBJMS | Singular Masculine Pronoun complements of objects | le lui | |
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| PPOBJMP | Plurial Masculine Pronoun complements of objects | eux y | |
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| PPOBJFS | Singular Feminine Pronoun complements of objects | moi la | |
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| PPOBJFP | Plurial Feminine Pronoun complements of objects | 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 Masculine Singular | il | |
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| PPER3MP | Personal Pronoun Third Person Masculine Plurial | ils | |
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| PPER3FS | Personal Pronoun Third Person Feminine Singular | elle | |
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| PPER3FP | Personal Pronoun Third Person Feminine Plurial | elles | |
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| PREFS | Reflexive Pronouns First Person of Singular | me m' | |
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| PREF | Reflexive Pronouns Third Person of Singular | se s' | |
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| PREFP | Reflexive Pronouns First / Second Person of Plurial | nous vous | |
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| VERB | Verb | obtient | |
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| VPPMS | Singular Masculine Participle Past Verb | formulé | |
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| VPPMP | Plurial Masculine Participle Past Verb | classés | |
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| VPPFS | Singular Feminine Participle Past Verb | appelée | |
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| VPPFP | Plurial Feminine Participle Past Verb | sanctionnées | |
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| DET | Determinant | les l' | |
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| DETMS | Singular Masculine Determinant | les | |
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| DETFS | Singular Feminine Determinant | la | |
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| ADJ | Adjective | capable sérieux | |
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| ADJMS | Singular Masculine Adjective | grand important | |
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| ADJMP | Plurial Masculine Adjective | grands petits | |
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| ADJFS | Singular Feminine Adjective | française petite | |
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| ADJFP | Plurial Feminine Adjective | légères petites | |
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| NOUN | Noun | temps | |
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| NMS | Singular Masculine Noun | drapeau | |
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| NMP | Plurial Masculine Noun | journalistes | |
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| NFS | Singular Feminine Noun | tête | |
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| NFP | Plurial Feminine Noun | ondes | |
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| PREL | Relative Pronoun | qui dont | |
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| PRELMS | Singular Masculine Relative Pronoun | lequel | |
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| PRELMP | Plurial Masculine Relative Pronoun | lesquels | |
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| PRELFS | Singular Feminine Relative Pronoun | laquelle | |
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| PRELFP | Plurial Feminine Relative Pronoun | 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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```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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