# Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging

arXiv: https://arxiv.org/abs/1809.03391 · HTML: https://mlsanity.com/paper/1809.03391/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models](https://mlsanity.com/paper/2201.00558/) (2022)
- [Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia](https://mlsanity.com/paper/2304.02746/) (2023)

## Abstract (arXiv)

Previous work in Indonesian part-of-speech (POS) tagging are hard to compare as they are not evaluated on a common dataset. Furthermore, in spite of the success of neural network models for English POS tagging, they are rarely explored for Indonesian. In this paper, we explored various techniques for Indonesian POS tagging, including rule-based, CRF, and neural network-based models. We evaluated our models on the IDN Tagged Corpus. A new state-of-the-art of 97.47 F1 score is achieved with a recurrent neural network. To provide a standard for future work, we release the dataset split that we used publicly.