README.md

tieval

A framework for evaluation and development of temporally aware models.

Installation

The package is available on PyPI:

pip install tieval

It requires Python 3.8 or above.

Usage

To understand its usability refer to the notebooks available here.

Data

Throughout the last two decades many datasets have been developed to train this task. tieval provides an easy interface to download the available corpus.

To know more about the module run the following code on the terminal.

python -m tieval download --help

How to ...

In this section, we summarize how to perform the most useful operations in tieval.

download a dataset.

from pathlib import Path
from tieval import datasets

data_path = Path("data/")
datasets.download("TimeBank", data_path)

load a dataset.

from tieval import datasets

te3 = datasets.read("tempeval_3")

load a model.

from tieval import models

model = models.TimexIdentificationBaseline()

make predictions.

pred = model.predict(te3.test)

evaluate predictions.

from tieval import evaluate

annot = {doc.name: doc.timexs for doc in te3.test}
results = evaluate.timex_identification(annot, pred)

Contributing

1. Fork it (https://github.com/LIAAD/tieval)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

Meta

Hugo Sousa - [email protected]

This framework is part of the Text2Story project which is financed by the ERDF – European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185)

Publications

If you use tieval in your work please site the following article:

@inproceedings{10.1145/3539618.3591892,
    author = {Sousa, Hugo and Campos, Ricardo and Jorge, Al\'{\i}pio M\'{a}rio},
    title = {Tieval: An Evaluation Framework for Temporal Information Extraction Systems},
    year = {2023},
    isbn = {9781450394086},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3539618.3591892},
    doi = {10.1145/3539618.3591892},
    booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages = {2871–2879},
    numpages = {9},
    keywords = {temporal information extraction, evaluation, python package},
    location = {Taipei, Taiwan},
    series = {SIGIR '23}
}