# A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign

arXiv: https://arxiv.org/abs/1803.08463 · HTML: https://mlsanity.com/paper/1803.08463/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments](https://mlsanity.com/paper/2504.21016/) (2025)

## Abstract (arXiv)

In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling problem using BIO encoding scheme. We applied a feature-based model which combines word, word-shape features, Brown-cluster-based features, and word-embedding-based features. We compare several methods to deal with nested entities in the dataset. We showed that combining tags of entities at all levels for training a sequence labeling model (joint-tag model) improved the accuracy of nested named-entity recognition.