An LMF conformant XML-based file containing the electronic version of al logha al arabia al moassira (Contemporary Arabic) dictionary. An Arabic monolingual dictionary accomplished by Ahmed Mukhtar Abdul Hamid Omar (deceased: 1424) with the help of a working group
Document-level testsuite for evaluation of gender translation consistency.
Our Document-Level test set consists of selected English documents from the WMT21 newstest annotated with gender information. Czech unnanotated references are also added for convenience.
We semi-automatically annotated person names and pronouns to identify the gender of these elements as well as coreferences.
Our proposed annotation consists of three elements: (1) an ID, (2) an element class, and (3) gender.
The ID identifies a person's name and its occurrences (name and pronouns).
The element class identifies whether the tag refers to a name or a pronoun.
Finally, the gender information defines whether the element is masculine or feminine.
We performed a series of NLP techniques to automatically identify person names and coreferences.
This initial process resulted in a set containing 45 documents to be manually annotated.
Thus, we started a manual annotation of these documents to make sure they are correctly tagged.
See README.md for more details.
Moroccan Dialect Electronic Dictionary (MDED) is an electronic lexicon containing almost 15000 MSA entries. They are written in Arabic letters and translated to Moroccan Arabic dialect. In addition, MDED entries are annotated useful metadata such as POS, Origin and root. MDED can be useful in some advanced NLP applications such as Machine translation and morphological analyzer.
Normalized Arabic Fragments for Inestimable Stemming (NAFIS) is an Arabic stemming gold standard corpus composed by a collection of texts, selected to be representative of Arabic stemming tasks and manually annotated.
The NottDeuYTSch corpus contains over 33 million words taken from approximately 3 million YouTube comments from videos published between 2008 to 2018 targeted at a young, German-speaking demographic and represents an authentic language snapshot of young German speakers. The corpus was proportionally sampled based on video category and year from a database of 112 popular German-speaking YouTube channels in the DACH region for optimal representativeness and balance and contains a considerable amount of associated metadata for each comment that enable further longitudinal cross-sectional analyses.
An XML-based file containing Arabic Stop-words respecting nouns syntax; particle nouns, signal nouns, separated pronouns and connected nouns
Citation: Driss Namly, Yasser Regragui, Karim Bouzoubaa. "Interoperable Arabic language resources building and exploitation in SAFAR platform". 13th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA) November 29th to December 2nd, 2016.