# An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition

arXiv: https://arxiv.org/abs/2005.07692 · HTML: https://mlsanity.com/paper/2005.07692/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 5 of 5)

- [FedAdapter: Efficient Federated Learning for Modern NLP](https://mlsanity.com/paper/2205.10162/) (2022)
- [Why KDAC? A general activation function for knowledge discovery](https://mlsanity.com/paper/2111.13858/) (2021)
- [VBART: The Turkish LLM](https://mlsanity.com/paper/2403.01308/) (2024)
- [Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT](https://mlsanity.com/paper/2205.08497/) (2022)
- [Unsupervised Behaviour Analysis of News Consumption in Turkish Media](https://mlsanity.com/paper/2202.02056/) (2022)

## Abstract (arXiv)

Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. NER is crucial not only in downstream language processing applications such as relation extraction and question answering but also in large scale big data operations such as real-time analysis of online digital media content. Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art results by formulating the task as a sequence tagging problem. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory and Transformer-based networks) proposed for Turkish NER tagging in the same setting. Our results demonstrate that transformer-based networks which can model long-range context overcome the limitations of BiLSTM networks where different input features at the character, subword, and word levels are utilized. We also propose a transformer-based network with a conditional random field (CRF) layer that leads to the state-of-the-art result (95.95% f-measure) on a common dataset. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages.