This repository describes a Named Entity Extractor, a proposed implementation of the Named Entity Recognition (NER) task for a given Portuguese written file (or a set of files) in PDF format. The model used for this task is based on the BERTimbau project [1], where the word embeddings (BERT) are combined with a CRF layer and trained on a Portuguese corpus. The base code is taken from the original project.

The entities classes are set as below:

• Place ("Local")
• Name ("Nome")
• Organization ("Organização")
• Time ("Tempo")
• Value ("Valor")

The final output is composed of:

• A list of all named entities extracted and its count for each class
• A corresponding wordcloud for each named entity class, as below (for class Time)

The list of the required python packages is found on utils/requirements.txt. For quick installation under the selected environment, simply run:

pip install -r requirements.txt

The pre-trained models can be downloaded from this link. The default path for storing the models is: data/input/model_checkpoint/.

The test environment was configured as following:

• Operational System: Ubuntu 18.04
• Python version: 3.6
• Java version: 8

GPU is desirable for faster implementation.

Notes: Java is required for handling PDF files by the tika package.

To implement the program at the default arguments, simply place the PDF document(s) into the input folder (default is data/input/raw/) and run:

python main.py

The default output folders are set as below:

• data/output/wordcloud/ (for wordclouds)
• data/output/entities/ (for list of entities)

python main.py
    --verbose 1 {0 for silent run}
    --input_path data/input/raw/ 
    --predictions_file data/input/temp/predictions.txt
    --entities_path data/output/entities/
    --wordcloud_path data/output/wordcloud/
    --labels_file data/input/labels/classes.txt
    --bert_model data/input/model_checkpoint/
    

[1] SOUZA, Fábio; NOGUEIRA, Rodrigo; LOTUFO, Roberto. Portuguese named entity recognition using BERT-CRF. arXiv preprint arXiv:1909.10649, 2019.