# Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging

arXiv: https://arxiv.org/abs/1806.10771 · HTML: https://mlsanity.com/paper/1806.10771/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2018.

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

- [UKTA: Unified Korean Text Analyzer](https://mlsanity.com/paper/2502.09648/) (2025)
- [Linguistically Informed Graph Model and Semantic Contrastive Learning for Korean Short Text Classification](https://mlsanity.com/paper/2603.03652/) (2026)
- [SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models](https://mlsanity.com/paper/2604.12377/) (2026)

## Abstract (arXiv)

Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise utilizes comprehensive prior linguistic knowledge (i.e., a dictionary of known morphological transformation forms, or actions). These models have been created with the assumption that character-level, dictionary-less morphological analysis was intractable due to the number of actions required. We present, in this study, a multi-stage action-based model that can perform morphological transformation and part-of-speech tagging using arbitrary units of input and apply it to the case of character-level Korean morphological analysis. Among models that do not employ prior linguistic knowledge, we achieve state-of-the-art word and sentence-level tagging accuracy with the Sejong Korean corpus using our proposed data-driven Bi-LSTM model.