# Beheshti-NER: Persian Named Entity Recognition Using BERT

arXiv: https://arxiv.org/abs/2003.08875 · HTML: https://mlsanity.com/paper/2003.08875/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian](https://mlsanity.com/paper/2104.07613/) (2021)
- [German BERT Model for Legal Named Entity Recognition](https://mlsanity.com/paper/2303.05388/) (2023)
- [Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data](https://mlsanity.com/paper/2104.01364/) (2021)
- [ELAB: Extensive LLM Alignment Benchmark in Persian Language](https://mlsanity.com/paper/2504.12553/) (2025)
- [Multi-BERT: Leveraging Adapters and Prompt Tuning for Low-Resource Multi-Domain Adaptation](https://mlsanity.com/paper/2404.02335/) (2024)
- [Improve Academic Query Resolution through BERT-based Question Extraction from Images](https://mlsanity.com/paper/2405.01587/) (2024)
- [Named Entity Recognition for the Kurdish Sorani Language: Dataset Creation and Comparative Analysis](https://mlsanity.com/paper/2511.22315/) (2025)
- [PersianAnonymizer: Evaluating LLM-Labeled Training for Efficient NER-based Anonymization in Persian](https://mlsanity.com/paper/2609.00958/) (2026)

## Abstract (arXiv)

Named entity recognition is a natural language processing task to recognize and extract spans of text associated with named entities and classify them in semantic Categories. Google BERT is a deep bidirectional language model, pre-trained on large corpora that can be fine-tuned to solve many NLP tasks such as question answering, named entity recognition, part of speech tagging and etc. In this paper, we use the pre-trained deep bidirectional network, BERT, to make a model for named entity recognition in Persian. We also compare the results of our model with the previous state of the art results achieved on Persian NER. Our evaluation metric is CONLL 2003 score in two levels of word and phrase. This model achieved second place in NSURL-2019 task 7 competition which associated with NER for the Persian language. our results in this competition are 83.5 and 88.4 f1 CONLL score respectively in phrase and word level evaluation.