# Curras + Baladi: Towards a Levantine Corpus

arXiv: https://arxiv.org/abs/2205.09692 · HTML: https://mlsanity.com/paper/2205.09692/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 3 of 3)

- [A Panoramic Survey of Natural Language Processing in the Arab World](https://mlsanity.com/paper/2011.12631/) (2020)
- [Extracting Synonyms from Bilingual Dictionaries](https://mlsanity.com/paper/2012.00600/) (2020)
- [LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation](https://mlsanity.com/paper/2104.08110/) (2021)

## Built on by (most cited first, 20 of 20)

- [AraFinNLP 2024: The First Arabic Financial NLP Shared Task](https://mlsanity.com/paper/2407.09818/) (2024)
- [Nabra: Syrian Arabic Dialects with Morphological Annotations](https://mlsanity.com/paper/2310.17315/) (2023)
- [A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms](https://mlsanity.com/paper/2302.02232/) (2023)
- [SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks](https://mlsanity.com/paper/2310.19029/) (2023)
- [Lisan: Yemeni, Iraqi, Libyan, and Sudanese Arabic Dialect Copora with Morphological Annotations](https://mlsanity.com/paper/2212.06468/) (2022)
- [ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic](https://mlsanity.com/paper/2310.19034/) (2023)
- [Arabic Fine-Grained Entity Recognition](https://mlsanity.com/paper/2310.17333/) (2023)
- [NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2509.02038/) (2025)
- [Offensive Hebrew Corpus and Detection using BERT](https://mlsanity.com/paper/2309.02724/) (2023)
- [WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task](https://mlsanity.com/paper/2310.16153/) (2023)
- [WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task](https://mlsanity.com/paper/2407.09936/) (2024)
- [NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness](https://mlsanity.com/paper/2405.00659/) (2024)
- [Event-Arguments Extraction Corpus and Modeling using BERT for Arabic](https://mlsanity.com/paper/2407.21153/) (2024)
- [Maknuune: A Large Open Palestinian Arabic Lexicon](https://mlsanity.com/paper/2210.12985/) (2022)
- [ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task](https://mlsanity.com/paper/2407.20663/) (2024)
- [QuranMorph: Morphologically Annotated Quranic Corpus](https://mlsanity.com/paper/2506.18148/) (2025)
- [Aladdin-FTI @ AMIYA Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation](https://mlsanity.com/paper/2602.16290/) (2026)
- [SinaTools: Open Source Toolkit for Arabic Natural Language Processing](https://mlsanity.com/paper/2411.01523/) (2024)
- [Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic Hate Speech Detection](https://mlsanity.com/paper/2412.10991/) (2024)
- [CARDAMOM: A Micro-Dialectal Arabic Speech Dataset for ASR](https://mlsanity.com/paper/2609.34481/) (2026)

## Abstract (arXiv)

The processing of the Arabic language is a complex field of research. This is due to many factors, including the complex and rich morphology of Arabic, its high degree of ambiguity, and the presence of several regional varieties that need to be processed while taking into account their unique characteristics. When its dialects are taken into account, this language pushes the limits of NLP to find solutions to problems posed by its inherent nature. It is a diglossic language; the standard language is used in formal settings and in education and is quite different from the vernacular languages spoken in the different regions and influenced by older languages that were historically spoken in those regions. This should encourage NLP specialists to create dialect-specific corpora such as the Palestinian morphologically annotated Curras corpus of Birzeit University. In this work, we present the Lebanese Corpus Baladi that consists of around 9.6K morphologically annotated tokens. Since Lebanese and Palestinian dialects are part of the same Levantine dialectal continuum, and thus highly mutually intelligible, our proposed corpus was constructed to be used to (1) enrich Curras and transform it into a more general Levantine corpus and (2) improve Curras by solving detected errors.