# A Feature-Rich Vietnamese Named-Entity Recognition Model

arXiv: https://arxiv.org/abs/1803.04375 · HTML: https://mlsanity.com/paper/1803.04375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [REBot: From RAG to CatRAG with Semantic Enrichment and Graph Routing](https://mlsanity.com/paper/2510.01800/) (2025)
- [A Hybrid Method for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2605.04489/) (2026)

## Abstract (arXiv)

In this paper, we present a feature-based named-entity recognition (NER) model that achieves the start-of-the-art accuracy for Vietnamese language. We combine word, word-shape features, PoS, chunk, Brown-cluster-based features, and word-embedding-based features in the Conditional Random Fields (CRF) model. We also explore the effects of word segmentation, PoS tagging, and chunking results of many popular Vietnamese NLP toolkits on the accuracy of the proposed feature-based NER model. Up to now, our work is the first work that systematically performs an extrinsic evaluation of basic Vietnamese NLP toolkits on the downstream NER task. Experimental results show that while automatically-generated word segmentation is useful, PoS and chunking information generated by Vietnamese NLP tools does not show their benefits for the proposed feature-based NER model.