# End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level

arXiv: https://arxiv.org/abs/1705.04044 · HTML: https://mlsanity.com/paper/1705.04044/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

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

- [Predicting Job Titles from Job Descriptions with Multi-label Text Classification](https://mlsanity.com/paper/2112.11052/) (2021)
- [Automatically Detecting Cyberbullying Comments on Online Game Forums](https://mlsanity.com/paper/2106.01598/) (2021)
- [Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding](https://mlsanity.com/paper/2109.01636/) (2021)

## Abstract (arXiv)

This paper demonstrates end-to-end neural network architectures for Vietnamese named entity recognition. Our best model is a combination of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), Conditional Random Field (CRF), using pre-trained word embeddings as input, which achieves an F1 score of 88.59% on a standard test set. Our system is able to achieve a comparable performance to the first-rank system of the VLSP campaign without using any syntactic or hand-crafted features. We also give an extensive empirical study on using common deep learning models for Vietnamese NER, at both word and character level.