PTOIE

A pt-br OpenIE corpus and model.

Setup Project:

1. First install python 3.9 and run pip install poetry.
2. Open the project, set a poetry python interpreter and run poetry update on venv terminal.
3. After that, you need to wait for poetry to install all the needed packages.

Train and Pred Usage:

Training the model

For training the model, first get your data ready, with train, dev and test splits. run the following command(you can replace with your own parameters):

python3 train.py (max_epochs) (model_name) (train_file) (test_file) (dev_file)

example1:

python3 train.py 150 PTOIE datasets/saida_match PTOIE_train.txt PTOIE_test.txt PTOIE_dev.txt

Making predictions

For predicting, at first, you need a trained model, so if you didnt trained any model, back on the training step. If you have your trained model, just run:

Python3 predict.py (model_name) (sentence)

example2:

Python3 predict.py PTOIE "A Matemática é uma ciência que utiliza conceitos e técnicas para a formação de conhecimentos abstratos e concretos."

Working example

|  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  |
Extração:  Os cachorros são os melhores amigos do homem
Extração:  Os cachorros são mamiferos
|  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  |


|  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  |
|  ------------------------------------------------------------------------------------------------ MAIS INFO -------------------------------------------------------------------------------------------------  |
|  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  |
|  sentença:                                                                                                                                                                                                     |
|   "Os cachorros , que são mamiferos , são os melhores amigos do homem ."                                                                                                                                       |
|  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  |
|  extrações:                                                                                                                                                                                                    |
|   ["Os cachorros"/ARG0, "são"/V, "mamiferos"/ARG1, "são"/V, "os melhores amigos do homem"/ARG1]                                                                                                                |
|  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  |
|  probs:                                                                                                                                                                                                        |
|  [Span[0:2]: "Os cachorros" → ARG0 (0.8672), Span[4:5]: "são" → V (0.6802), Span[5:6]: "mamiferos" → ARG1 (0.3477), Span[7:8]: "são" → V (0.9333), Span[8:13]: "os melhores amigos do homem" → ARG1 (0.5932)]   |
|  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  |

Evaluation

You can run the eval script with the trained model:

python3 eval.py (model_dir) (output_txt_name) (corpus_dir) (train_path) (test_path) (dev_path)

example5:

python3 eval.py train_output/PTOIE PTOIE_eval datasets/saida_match PTOIE_train.txt PTOIE_test.txt PTOIE_dev.txt

output:

Results:
- F-score (micro) 0.6129
- F-score (macro) 0.6132
- Accuracy 0.4425

By class:
              precision    recall  f1-score   support

        ARG1     0.4731    0.5163    0.4938       153
           V     0.7453    0.7895    0.7668       152
        ARG0     0.5972    0.5621    0.5791       153

   micro avg     0.6038    0.6223    0.6129       458
   macro avg     0.6052    0.6226    0.6132       458
weighted avg     0.6049    0.6223    0.6129       458

Dataset tools

I made available some tools I wrote to convert and create a conll format dataset, feel free to use, some of them is not user friendly to use, but you can run the main.py to prepare a conll with the labels ready to train with:

• The following command only works with the PTOIE dataset, if you want to use your own dataset, see explanation of example4.

python3 datasets/main.py (output_name) (json_dir) (input_dir) (test_size) (dev_size)

example3:

PTOIE saida_match/json_dump.json PTOIE/PTOIE.txt 0.1 0.1

If you want to create a conll of your own dataset, you need a json on the following format example:

{"0": 
  {"Id": 0,
   "sent": " A universidade \u00e9 a sede principal da Congrega\u00e7\u00e3o da Santa Cruz (embora n\u00e3o seja sua sede oficial, que fica em Roma).",
   "ext": [{"arg1": "A sede da congrega\u00e7\u00e3o da santa cruz  ", "rel": "   fica  ", "arg2": "  em Roma"}]},
"1": 
  {"Id": 1,
   "sent": " A universidade \u00e9 afiliada \u00e0 Congrega\u00e7\u00e3o da Santa Cruz (em latim Congregatio a Sancta Cruce, p\u00f3s-nominais abreviados \"CSC\").",
   "ext": [{"arg1": "A congrega\u00e7\u00e3o da santa cruz em latim  ", "rel": "   \u00e9  ", "arg2": "  Congregatio a Sancta Cruce"}]},

With the json file, run the following command

python3 datasets/main.py (output_name) (test_size) (dev_size) (json_dir) ""

example4:

python3 datasets/main.py PTOIE 0.1 0.1 saida_match/json_dump.json ""

Authors

• A. Rios
• B. Cabral
• D. B. Claro
• R. C. Araujo
• M. Souza

How to cite

If you find this repo helpful, please consider citing:

@inproceedings{RIOS2024-TransAlign,
title={TransAlign: An Automated Corpus Generation through Cross-Linguistic Data Alignment for Open Information Extraction},
author={A. Rios and B. Cabral and D. B. Claro and R. C. Araujo and M. Souza},
booktitle={Proceedings of the International Conference on Computational Processing of Portuguese (PROPOR 2024)},
year={2024},
volume={1},
address={Santiago de Compostela},
organization={International Conference on Computational Processing of Portuguese (PROPOR 2024)}
}

• A. Rios, B. Cabral, D. B. Claro, R. C. Araujo, M. Souza: TransAlign: An Automated Corpus Generation through Cross-Linguistic Data Alignment for Open Information Extraction. In: Proceedings of the International Conference on Computational Processing of Portuguese (PROPOR 2024), Santiago de Compostela, 2024, vol. 1.