widaug

Data Augmentation for NLP tasks using wikidata

https://github.com/oeg-upm/widaug

Science Score: 57.0%

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Repository

Data Augmentation for NLP tasks using wikidata

Basic Info
  • Host: GitHub
  • Owner: oeg-upm
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 1.54 MB
Statistics
  • Stars: 3
  • Watchers: 6
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created almost 4 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Widaug

Widaug is a data augmentation system for named entity recognition (NER) using Wikdiata.

Install

python setup.py install

Experiments

The core of the experiment performed for the SEPLN paper is provided in the folder experiments/sepln2022

Acknowledgments

Financed by the European UnionNextGenerationEU (UP2021-035), by the SmarTerp Project (EIT-Digital21184) and by the project HCommonK(RTC2019-007134-7, funded by MCIN/AEI/10.13039/501100011033).

Paper Citation

bibtext @article{calleja2023widaug, title={Widaug. Data augmentation for named entity recognition using Wikidata}, author={Calleja, Pablo and S{\'a}nchez, Alberto and Corcho, Oscar}, journal={Procesamiento del Lenguaje Natural}, volume={70}, pages={145--155}, year={2023} }

Owner

  • Name: Ontology Engineering Group (UPM)
  • Login: oeg-upm
  • Kind: organization
  • Email: oeg-dev@delicias.dia.fi.upm.es
  • Location: Boadilla del Monte, Madrid, Spain

Citation (CITATION.cff)

cff-version: 1.2.0
message: "Si utilizas este trabajo, por favor cita el siguiente artículo:"
title: "Widaug. Data augmentation for named entity recognition using Wikidata"
authors:
  - family-names: Calleja
    given-names: P.
  - family-names: Sánchez
    given-names: A.
  - family-names: Corcho
    given-names: O.
date-released: 2023-09-01
version: "1.0.0"
doi: 10.26342/2023-70-12
url: https://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6489
type: article
journal: Procesamiento del Lenguaje Natural
volume: 70
startpage: 145
endpage: 155

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Dependencies

requirements.txt pypi
  • datasets ==2.4.0
  • seqeval ==1.2.2
  • transformers ==4.21.0
setup.py pypi
  • line.strip *