Camelira: An Arabic Multi-Dialect Morphological Disambiguator (2022.emnlp-demos)
Copied to clipboard
| Challenge: | Camelira is a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Approach: | They propose a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Outcome: | The proposed tool covers modern standard Arabic, Egyptian, Gulf, and Levantine . it also provides an option to automatically choose an appropriate disambiguator based on the prediction of a dialect identification component. |
Similar Papers
Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods (2020.lrec-1)
Copied to clipboard
| Challenge: | Despite advances in the field of natural language processing, many dialectal Arabic varieties are lagging behind . despite advances in NLP, many Arabic dialects are considered under-resourced . |
| Approach: | They propose a full morphological analysis and disambiguation system for Gulf Arabic . they use existing state-of-the-art morphology tools to investigate the effects of different data sizes and combinations of morphologists. |
| Outcome: | The proposed system improves on the existing system for Arabic . it is based on a set of data sizes and combinations of morphological analyzers . |
The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic (2022.lrec-1)
Copied to clipboard
| Challenge: | Various corpora of various sizes and representing different genres, have been created for various Arabic dialects. |
| Approach: | They propose to create a specialized corpus of Bahraini Arabic dialect, which includes written texts as well as transcripts of audio files. |
| Outcome: | The proposed corpus includes 620K words representing the Bahraini Arabic dialect . the annotated corpus is available to support researchers interested in Arabic NLP . |
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)
Copied to clipboard
Bashar Talafha, Karima Kadaoui, Samar Magdy, Mariem Habiboullah, Chafei Chafei, Ahmed El-Shangiti, Hiba Zayed, Mohamedou Tourad, Rahaf Alhamouri, Rwaa Assi, Aisha Alraeesi, Hour Mohamed, Fakhraddin Alwajih, Abdelrahman Mohamed, Abdellah El Mekki, El Moatez Billah Nagoudi, Benelhadj Saadia, Hamzah Alsayadi, Walid Al-Dhabyani, Sara Shatnawi, Yasir Ech-chammakhy, Amal Makouar, Yousra Berrachedi, Mustafa Jarrar, Shady Shehata, Ismail Berrada, Muhammad Abdul-Mageed
| Challenge: | despite recent advances in speech processing, the majority of world languages and dialects remain uncovered. |
| Approach: | They propose to collect and transcribe a new Arabic dataset for eight dialects . they also develop strong baselines exploiting the new dataset . |
| Outcome: | The proposed dataset covers eight Arabic dialects, including Algerian, Egyptian, Emirati, Jordanian, Mauritanian, Moroccan, Palestinian, and Yemeni. |
Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic (2024.lrec-main)
Copied to clipboard
| Challenge: | Camel Morph MSA is the largest open-source Modern Standard Arabic morphological analyzer and generator. |
| Approach: | They present Camel Morph MSA, the largest open-source Arabic morphological analyzer and generator. |
| Outcome: | The analysis can produce 1.45B analyses and 535M unique diacritizations, almost an order of magnitude larger than SAMA on a 10B word corpus. |
ADIDA: Automatic Dialect Identification for Arabic (N19-4)
Copied to clipboard
| Challenge: | Demo paper describes a web-based system for automatic dialect identification for Arabic text. |
| Approach: | They present a web-based system for automatic dialect identification for Arabic text that distinguishes between 25 Arab cities and Modern Standard Arabic. |
| Outcome: | The proposed system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) and Modern Standard Arabic (MSA). |
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing (2020.lrec-1)
Copied to clipboard
Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash
| Challenge: | CAMeL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Approach: | They present CAMeL Tools, an open-source Python toolkit for Arabic natural language processing . CAMeleL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Outcome: | The proposed tools are based on CAMeL Tools, an open-source Python toolkit for Arabic natural language processing. |
A Spelling Correction Corpus for Multiple Arabic Dialects (2020.lrec-1)
Copied to clipboard
| Challenge: | Arabic dialects are non-standard varieties of Arabic commonly spoken across the Arab world, but lack standard orthographies. |
| Approach: | They present a corpus of 10,000 sentences from five Arabic city dialects represented in the Conventional Orthography for Dialectal Arabic (CODA) they use a bootstrapping technique to speed up annotation and compare similarity between dialects before and after CODA annotation. |
| Outcome: | The proposed method speeds up the annotation process and shows similarity between the dialects before and after CODA annotation. |
A description and demonstration of SAFAR framework (2021.eacl-demos)
Copied to clipboard
Karim Bouzoubaa, Younes Jaafar, Driss Namly, Ridouane Tachicart, Rachida Tajmout, Hakima Khamar, Hamid Jaafar, Lhoussain Aouragh, Abdellah Yousfi
| Challenge: | Existing NLP infrastructures are naming them "toolkit", "platform" and "framework" authors present a monolingual framework dedicated to Arabic language . |
| Approach: | They propose a monolingual framework dedicated to Arabic language . they propose namings for existing infrastructures: "toolkit", "platform" and "framework" |
| Outcome: | The proposed framework is dedicated to Arabic language, especially the modern standard Arabic and Moroccan dialect. |
MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction (L18-1)
Copied to clipboard
| Challenge: | Standard Arabic morphology is rich, but Arabic dialects introduce more complexity. |
| Approach: | They propose a joint morphological annotation and spelling correction system for Arabic texts . they propose morphology tools that can be used to help with productivity . |
| Outcome: | The proposed system is based on a standard and dialectal Arabic text. |
Automatic Identification of Maghreb Dialects Using a Dictionary-Based Approach (L18-1)
Copied to clipboard
| Challenge: | Automatic identification of Arabic dialects in texts is difficult, especially for Maghreb languages and when they are written in Arabic or Latin characters (Arabizi). |
| Approach: | They propose a dictionary-based approach to detect Arabic dialects in texts . they focus on transliteration of Arabicizi into Latin script and code-switching . |
| Outcome: | The proposed approach shows that it is possible to detect dialects in Arabic and Latin scripts. |