POS-Tagger Portuguese
We fine-tuned the BERTimbau model with the MacMorpho corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.9826.
Metrics:
Precision Recall F1 Suport
accuracy 0.98 33729
macro avg 0.96 0.95 0.95 33729
weighted avg 0.98 0.98 0.98 33729
F1: 0.9826 Accuracy: 0.9826
Parameters:
nclasses = 27
nepochs = 30
batch_size = 32
batch_status = 32
learning_rate = 1e-5
early_stop = 3
max_length = 200
Tags:
| Tag | Meaning |
|---|---|
| ADJ | Adjetivo |
| ADV | Advérbio |
| ADV-KS | Advérbio conjuntivo subordinado |
| ADV-KS-REL | Advérbio relativo subordinado |
| ART | Artigo |
| CUR | Moeda |
| IN | Interjeição |
| KC | Conjunção coordenativa |
| KS | Conjunção subordinativa |
| N | Substantivo |
| NPROP | Substantivo próprio |
| NUM | Número |
| PCP | Particípio |
| PDEN | Palavra denotativa |
| PREP | Preposição |
| PROADJ | Pronome Adjetivo |
| PRO-KS | Pronome conjuntivo subordinado |
| PRO-KS-REL | Pronome relativo conectivo subordinado |
| PROPESS | Pronome pessoal |
| PROSUB | Pronome nominal |
| V | Verbo |
| VAUX | Verbo auxiliar |
How to cite
@article{
Schneider_postagger_2023,
place={Brasil},
title={Developing a Transformer-based Clinical Part-of-Speech Tagger for Brazilian Portuguese},
volume={15},
url={https://jhi.sbis.org.br/index.php/jhi-sbis/article/view/1086},
DOI={10.59681/2175-4411.v15.iEspecial.2023.1086},
abstractNote={<p>Electronic Health Records are a valuable source of information to be extracted by means of natural language processing (NLP) tasks, such as morphosyntactic word tagging. Although there have been significant advances in health NLP, such as the Transformer architecture, languages such as Portuguese are still underrepresented. This paper presents taggers developed for Portuguese texts, fine-tuned using BioBERtpt (clinical/biomedical) and BERTimbau (generic) models on a POS-tagged corpus. We achieved an accuracy of 0.9826, state-of-the-art for the corpus used. In addition, we performed a human-based evaluation of the trained models and others in the literature, using authentic clinical narratives. Our clinical model achieved 0.8145 in accuracy compared to 0.7656 for the generic model. It also showed competitive results compared to models trained specifically with clinical texts, evidencing domain impact on the base model in NLP tasks.</p>},
number={Especial}, journal={Journal of Health Informatics},
author={Schneider, Elisa Terumi Rubel and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Montenegro, Carolina de Oliveira and Barzotto, Laura Rubel and Moro, Claudia and Pagano, Adriana and Paraiso, Emerson Cabrera},
year={2023},
month={jul.} }
Questions?
Please, post a Github issue on the NLP Portuguese POS-Tagger.