ANETAC: Arabic Named Entity Transliteration and Classification Dataset
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Abstract (arXiv)
In this paper, we make freely accessible ANETAC our English-Arabic named entity transliteration and classification dataset that we built from freely available parallel translation corpora. The dataset contains 79,924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that can be either a Person, a Location, or an Organization. The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2019.
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Citing papers per year: 2023: 4; 2024: 0; 2025: 1.
5 papers.
- 2023GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLPcited by 37
- 2023Dolphin: A Challenging and Diverse Benchmark for Arabic NLGcited by 22
- 2023Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generationcited by 6
- 2025MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMscited by 4
- 2023Improving the Quality of Neural Machine Translation Through Proper Translation of Name Entitiescited by 1
- 2025MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs26 May 2025
- 2023Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation24 Oct 2023
- 2023Dolphin: A Challenging and Diverse Benchmark for Arabic NLG24 May 2023
- 2023GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP24 May 2023
- 2023Improving the Quality of Neural Machine Translation Through Proper Translation of Name Entities12 May 2023