SEACrowd/pfsa_id

PFSA-ID is an annotated corpus for Public Figure Statement Attribution in the Indonesian Language.

The annotation using the multi-class named entity recognition with 11 labels: PERSON, ROLE, AFFILIATION, PERSONCOREF, CUE, CUECOREF, STATEMENT, ISSUE, EVENT, DATETIME, and LOCATION and using the BILOU scheme as the representation of tokens.

Languages

ind

Supported Tasks

Named Entity Recognition, Pos Tagging

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/pfsa_id", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("pfsa_id", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("pfsa_id"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")

More details on how to load the seacrowd library can be found here.

Dataset Homepage

https://github.com/sigit-purnomo/pfsa-id

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution Non Commercial Share Alike 4.0 (cc-by-nc-sa-4.0)

Citation

If you are using the Pfsa Id dataloader in your work, please cite the following:

@article{PURNOMOWP2022,
  title = {PFSA-ID: an annotated Indonesian corpus and baseline model of public figures statements attributions},
  journal = {Global Knowledge, Memory and Communication},
  volume = {ahead-of-print},
  pages = {ahead-of-print},
  year = {2022},
  issn = {2514-9342},
  doi = {https://doi.org/10.1108/GKMC-04-2022-0091},
  url = {https://www.emerald.com/insight/content/doi/10.1108/GKMC-04-2022-0091/full/html},
  author = {Yohanes Sigit {Purnomo W.P.} and Yogan Jaya Kumar and Nur Zareen Zulkarnain},
  keywords = {Indonesian corpus, Public figures, Statement attribution, News article, Baseline model, Named entity recognition},
  abstract = {Purpose By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a
    baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language. Design/methodology/approach The methodology is divided into corpus development and extraction model
    development. During corpus development, data were collected and annotated. The development of the extraction model entails feature extraction, the definition of the model architecture, parameter selection and configuration,
    model training and evaluation, as well as model selection. Findings The Indonesian corpus of public figure statements attribution achieved 90.06% agreement level between the annotator and experts and could serve as a gold standard corpus.
    Furthermore, the baseline model predicted most labels and achieved 82.026% F-score. Originality/value To the best of the authors’ knowledge, the resulting corpus is the first corpus for attribution of public figures’ statements in the Indonesian
    language, which makes it a significant step for research on attribution extraction in the language. The resulting corpus and the baseline model can be used as a benchmark for further research. Other researchers could follow the methods presented
    in this paper to develop a new corpus and baseline model for other languages.
    }
}

@article{PurnomoWP2024,
  title = {Extraction and attribution of public figures statements for journalism in Indonesia using deep learning},
  volume = {289},
  ISSN = {0950-7051},
  url = {http://dx.doi.org/10.1016/j.knosys.2024.111558},
  DOI = {10.1016/j.knosys.2024.111558},
  journal = {Knowledge-Based Systems},
  publisher = {Elsevier BV},
  author = {Purnomo W.P.,  Yohanes Sigit and Kumar,  Yogan Jaya and Zulkarnain,  Nur Zareen and Raza,  Basit},
  year = {2024},
  month = apr,
  pages = {111558}
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}