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.