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indolem_ner_ugm
NER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity.
Dataset Usage
Run pip install nusacrowd before loading the dataset through HuggingFace's load_dataset.
Citation
@inproceedings{koto-etal-2020-indolem,
title = "{I}ndo{LEM} and {I}ndo{BERT}: A Benchmark Dataset and Pre-trained Language Model for {I}ndonesian {NLP}",
author = "Koto, Fajri and
Rahimi, Afshin and
Lau, Jey Han and
Baldwin, Timothy",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.66",
doi = "10.18653/v1/2020.coling-main.66",
pages = "757--770"
}
@phdthesis{fachri2014pengenalan,
title = {Pengenalan Entitas Bernama Pada Teks Bahasa Indonesia Menggunakan Hidden Markov Model},
author = {FACHRI, MUHAMMAD},
year = {2014},
school = {Universitas Gadjah Mada}
}
License
Creative Commons Attribution 4.0
Homepage
NusaCatalogue
For easy indexing and metadata: https://indonlp.github.io/nusa-catalogue
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