# From Word Segmentation to POS Tagging for Vietnamese

arXiv: https://arxiv.org/abs/1711.04951 · HTML: https://mlsanity.com/paper/1711.04951/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Span Labeling Approach for Vietnamese and Chinese Word Segmentation](https://mlsanity.com/paper/2110.00156/) (2021)
- [Span Detection for Aspect-Based Sentiment Analysis in Vietnamese](https://mlsanity.com/paper/2110.07833/) (2021)
- [Augmenting Part-of-speech Tagging with Syntactic Information for Vietnamese and Chinese](https://mlsanity.com/paper/2102.12136/) (2021)
- [Is word segmentation necessary for Vietnamese sentiment classification?](https://mlsanity.com/paper/2301.00418/) (2023)
- [A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension](https://mlsanity.com/paper/2506.15978/) (2025)
- [REBot: From RAG to CatRAG with Semantic Enrichment and Graph Routing](https://mlsanity.com/paper/2510.01800/) (2025)

## Abstract (arXiv)

This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable. We also make a comparison between state-of-the-art (SOTA) feature-based and neural network-based models. On the benchmark Vietnamese treebank (Nguyen et al., 2009), experimental results show that the pipeline strategy produces better scores of POS tagging from unsegmented text than the joint strategy, and the highest accuracy is obtained by using a feature-based model.