# Neural Named Entity Recognition for Kazakh

arXiv: https://arxiv.org/abs/2007.13626 · HTML: https://mlsanity.com/paper/2007.13626/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

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

- [KazNERD: Kazakh Named Entity Recognition Dataset](https://mlsanity.com/paper/2111.13419/) (2021)

## Abstract (arXiv)

We present several neural networks to address the task of named entity recognition for morphologically complex languages (MCL). Kazakh is a morphologically complex language in which each root/stem can produce hundreds or thousands of variant word forms. This nature of the language could lead to a serious data sparsity problem, which may prevent the deep learning models from being well trained for under-resourced MCLs. In order to model the MCLs' words effectively, we introduce root and entity tag embedding plus tensor layer to the neural networks. The effects of those are significant for improving NER model performance of MCLs. The proposed models outperform state-of-the-art including character-based approaches, and can be potentially applied to other morphologically complex languages.