Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.
In 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.
ARAB-ACQUIS is a large dataset for evaluating machine translation between 22 European languages and Arabic. ARAB-ACQUIS consists of over 12,000 sentences from the JRC-ACQUIS (Acquis Communautaire) corpus translated twice by professional translators, once from English and once from French, and totaling over 600,000 words.
The Arabic Parallel Gender Corpus is a corpus designed to support research on gender bias in natural language processing applications working on Arabic. It contains over 12,000 sentences annotated for first person singular gender, and over 200,000 synthetic sentences in masculine and feminine form.
Habash, Nizar, Houda Bouamor, Christine Chung. 2019. Automatic Gender Identification and Reinflection in Arabic. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, Florence, Italy.
CAMeL researchers have collected a 100M word corpus of Gulf Arabic, named the GUMAR Corpus. CAMeL Researchers are working on the automatic annotation of the corpus and on developing gold data annotations for 200,000 words from eight novels. Work on GUMAR will lead to improved tools for Gulf Arabic automatic analysis.
CAMeL Lab has also made Gumar n-grams available for download. These N-grams are in order of 5, that is 5, 4, 3, 2 and 1 grams with their respective frequency counts and number of documents they appear in. The format of the n-gram files follows a similar format of Google n-grams.
Khalifa, Salam, Nasser Zalmout, and Nizar Habash. Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods. In The Proceedings of LREC 2020.
Khalifa, Salam, et al. "A Morphologically Annotated Corpus of Emirati Arabic." LREC. 2018.
The Margarita Dialogue Corpus is a collection of question-answer pairs defined both outside the context of a conversation and in the context of dialogues between one person and different people. This corpus is part of a methodology developed for creating the knowledge base for time-offset interaction applications and unstructured dialogue systems.
Chierici, Alberto, Nizar Habash, and Margarita Bicec. "The Margarita Dialogue Corpus: A Data Set for Time-Offset Interactions and Unstructured Dialogue Systems." In Proceedings of The 12th Language Resources and Evaluation Conference, pp. 476-484. 2020.
The MADAR project (Multi-Arabic Dialect Applications and Resources) is a three year project that is a collaboration between CAMeL lab, the NLP group at Carnegie Mellon University in Qatar and Columbia University.
The MADAR Corpus is a collection of parallel sentences covering the dialects of 25 cities from the Arab World, in addition to English, French, and Modern Standard Arabic (MSA). This corpus is created by translating selected sentences from the Basic Traveling Expression Corpus (BTEC) in French and English to the different dialects.
The MADAR Corpus is made available to the research community under a non-commercial license.
CAMeL researchers are currently developing a treebank of texts annotated in the Universal Dependency syntactic representation. The NYUAD Arabic UD treebank (NUDAR) is based on the Penn Arabic Treebank (PATB), parts 1, 2, and 3, through conversion to CATiB dependency trees.
A dependency treebank of travel domain sentences in Modern Standard Arabic. The treebank dependency representation is in the style of the Columbia Arabic Treebank.
To obtain the Palm Treebank, please contact Dr. Nizar Habash.
A corpus of manually corrected Arabic text for building automatic correction tools.
The Arabic Multidialectal Word Embeddings are a set of pre-trained word embeddings learned from different dialects of Arabic. Individual dialects are mapped into each other's dialectal vector spaces to enable inter-dialectal comparison. A dialect agnostic model is included as well, learned from the combined corpora of all dialects. Lastly, we include the seed and evaluation dictionaries used to perform and evaluate the mappings.
ArabScribe was a senior capstone project interested in addressing the problem of language learners looking up unfamiliar words that they heard. The project created an improved phonetic search algorithm, and a data set of 10,000 impressions on native and non-native Arabic speakers of what a word is based on its audio alone.
Zhang, Lingliang, Nizar Habash, and Godfried Toussaint. "Robust Dictionary Lookup in Multiple Noisy Orthographies." Proceedings of the Third Arabic Natural Language Processing Workshop. 2017.
The MADAR project (Multi-Arabic Dialect Applications and Resources) is a three year project that is a collaboration between CAMeL lab, the NLP group at Carnegie Mellon University in Qatar and Columbia University.
The MADAR lexicon is a collection of 1,045 concepts extracted from the MADAR Corpus defined in terms of triplets of words and phrases from English, French and MSA, along with multiple equivalent dialectal forms covering 25 cities from the Arab World. Each dialectal form includes its CODA orthography and CAPHI phonology.
The SAMER readability lexicon is a large-scale 26,000-lemma leveled readability lexicon for Modern Standard Arabic. The lexicon was manually annotated in triplicate by language professionals from three regions in the Arab world.
Al Khalil, Muhamed, Nizar Habash, Zhengyang Jiang. "A Large-Scale Leveled Readability Lexicon for Standard Arabic." In Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 3053-3062 Marseille, 2020.
BOTTA is the first Arabic dialect chatbot, exploring the challenges of creating a conversational agent that aims to simulate friendly conversations using the Egyptian Arabic dialect.
A rich Arabic morphological analyzer and generator that provides functional and form-based morphological features as well as built-in tokenization, phonological representation, lexical rationality, reinflection, and much more.
The CamelParser is an Arabic syntactic dependency parser. It produces morphologically enriched Columbia Arabic Treebank (CATiB) syntactic representations.
An Open Source Python Toolkit for Arabic Natural Language Processing. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis.
Obeid, Ossama, et al. "CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing." Proceedings of The 12th Language Resources and Evaluation Conference. 2020.
CAMeL researchers developed a Chrome Extension tool that supports learning Arabic and Arabic dialects by providing translations and word analysis for words on any web page.
The Gulf Arabic morphological analyzer (CALIMAGLF) currently covers over 2,600 verbal lemmas. It models fully inflected paradigms and orthographic varieties.
The MADAR Annotaion Interface (MADARi) is a web-based framework for joint morphological annotation and spelling adjustment.
To obtain access to MADARi, please contact Dr. Nizar Habash.
Palmyra is a platform-independent graphical tool for syntactic dependency annotation supporting languages that require complex morphological tokenization.
Dima Taji and Nizar Habash. PALMYRA 2.0: A Configurable Multilingual Platform Independent Tool for Morphology and Syntax Annotation. In Proceedings of Universal Dependencies Workshop (UDW) 2020.
Talha Javed, Nizar Habash, and Dima Taji. 2018. Palmyra: A Platform Independent Dependency Annotation Tool for Morphologically Rich Languages. In Proceedings of LREC 2018.
Aiming towards a semantic representation of cooking recipes, Simplified Ingredient Merging Map in Recipes (SIMMR) proposes an ingredient-instruction dependency tree, and contains an automatic recipe parser and database.
YAMAMA (Yet Another Multi- Dialect Arabic Morphological Analyzer; Arabic يمامة ‘Barbary Dove’), is a multi-dialect Arabic morphological analyzer and disambiguator, that is five times faster than the state-of-the-art MADAMIRA with a slightly lower quality.
To obtain YAMAMA, please contact Dr. Nizar Habash.
The CAMeL Arabic Phonetic Inventory (CAPHI) is a system for representing, i.e. transcribing, the production of Arabic utterances in any dialect, from Modern Standard Arabic (MSA) to the regional colloquial varieties. CAPHI represents every significant sound in all Arabic dialects with a unique letter, meaning that it can be used to represent different pronunciations of words that would otherwise be spelled in the same way according to MSA, CODA*, Arabizi, or other Arabic spelling standards.
CAMEL POS is inspired by the ARZATB tagset and guidelines (Maamouri et al., 2012) which is based on the PATB guidelines (Maamouri et al., 2009). The CAMEL POS is designed as single tagset for both MSA and the dialects with the following goals in mind: (a) facilitating research on adaptation between MSA and the dialects, and among the dialects; (b) supporting backward compatibility with previously annotated resources; and (c) enforcing a functional morphology analysis that is deeper and more compatible with Arabic morphosyntactic rules than form based analysis (Alkuhlani and Habash, 2011). The CAMEL POS tags and features are the union of those in MSA and the dialects. Features are available to use when needed.
The Columbia Arabic Treebank (CATiB) formalism is a syntactic dependency representation used for the Arabic language.
CATiB contrasts with previous approaches to Arabic treebanking in its emphasis on speed with some constraints on linguistic richness. Two basic ideas inspire the CATiB approach: minimizing annotation of redundant information and using representations and terminology inspired by traditional Arabic syntax.
