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--- |
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license: cc-by-4.0 |
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tags: |
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- named-entity-recognition |
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language: |
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- ind |
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--- |
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# indolem_ner_ugm |
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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. |
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## Dataset Usage |
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Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. |
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## Citation |
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``` |
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@inproceedings{koto-etal-2020-indolem, |
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title = "{I}ndo{LEM} and {I}ndo{BERT}: A Benchmark Dataset and Pre-trained Language Model for {I}ndonesian {NLP}", |
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author = "Koto, Fajri and |
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Rahimi, Afshin and |
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Lau, Jey Han and |
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Baldwin, Timothy", |
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booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", |
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month = dec, |
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year = "2020", |
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address = "Barcelona, Spain (Online)", |
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publisher = "International Committee on Computational Linguistics", |
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url = "https://aclanthology.org/2020.coling-main.66", |
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doi = "10.18653/v1/2020.coling-main.66", |
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pages = "757--770" |
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} |
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@phdthesis{fachri2014pengenalan, |
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title = {Pengenalan Entitas Bernama Pada Teks Bahasa Indonesia Menggunakan Hidden Markov Model}, |
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author = {FACHRI, MUHAMMAD}, |
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year = {2014}, |
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school = {Universitas Gadjah Mada} |
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} |
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``` |
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## License |
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Creative Commons Attribution 4.0 |
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## Homepage |
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[https://indolem.github.io/](https://indolem.github.io/) |
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### NusaCatalogue |
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For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
