language:ind license:cc-by-4.0 named-entity-recognition arxiv:2011.00677 region:us
NER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.
The corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/170/42 and based on 5-fold cross validation.
@INPROCEEDINGS{8275098,
  author={Gultom, Yohanes and Wibowo, Wahyu Catur},
  booktitle={2017 International Workshop on Big Data and Information Security (IWBIS)},
  title={Automatic open domain information extraction from Indonesian text},
  year={2017},
  volume={},
  number={},
  pages={23-30},
  doi={10.1109/IWBIS.2017.8275098}}

@article{DBLP:journals/corr/abs-2011-00677,
  author    = {Fajri Koto and
               Afshin Rahimi and
               Jey Han Lau and
               Timothy Baldwin},
  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
               Model for Indonesian {NLP}},
  journal   = {CoRR},
  volume    = {abs/2011.00677},
  year      = {2020},
  url       = {https://arxiv.org/abs/2011.00677},
  eprinttype = {arXiv},
  eprint    = {2011.00677},
  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
---
license: cc-by-4.0
tags:
- named-entity-recognition
language:
- ind
---

# indolem_nerui

NER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.

The corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/170/42 and based on 5-fold cross validation.

## Dataset Usage

Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.

## Citation

```
@INPROCEEDINGS{8275098,
  author={Gultom, Yohanes and Wibowo, Wahyu Catur},
  booktitle={2017 International Workshop on Big Data and Information Security (IWBIS)},
  title={Automatic open domain information extraction from Indonesian text},
  year={2017},
  volume={},
  number={},
  pages={23-30},
  doi={10.1109/IWBIS.2017.8275098}}

@article{DBLP:journals/corr/abs-2011-00677,
  author    = {Fajri Koto and
               Afshin Rahimi and
               Jey Han Lau and
               Timothy Baldwin},
  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
               Model for Indonesian {NLP}},
  journal   = {CoRR},
  volume    = {abs/2011.00677},
  year      = {2020},
  url       = {https://arxiv.org/abs/2011.00677},
  eprinttype = {arXiv},
  eprint    = {2011.00677},
  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

## License

Creative Commons Attribution 4.0

## Homepage

[https://indolem.github.io/](https://indolem.github.io/)

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)