Arabic 
English 
French 
Spanish 
German 
Portuguese 
Turkish 
What is Tarjamaan?
Tarjamaan is a translation tool that allows you to look up the translation of millions of words and phrases to and from Arabic. Tarjamaan offers three main services:
Dictionaries
Dictionaries created specifically to take into account the peculiarities of the Arabic language.
Contextual translations
Millions of translations automatically extracted from parallel corpora (like UN documents). For each of these translations, a frequency indication is given, and several bilingual example sentences illustrate the original contexts.
Wikipedia
Search the translational equivalences of a term or an phrase in Wikipedia articles.
Statistics
Arabic-English contextual translations
bilingual Arabic-English example sentences
Arabic-French contextual translations
bilingual Arabic-French example sentences
translations in the Arabic-English dictionary
translations in the Arabic-French dictionary
Arabic-Spanish contextual translations
bilingual Arabic-Spanish example sentences
Arabic-German contextual translations
bilingual Arabic-German example sentences
Arabic-Portuguese contextual translations
bilingual Arabic-Portuguese example sentences
Arabic-Turkish contextual translations
bilingual Arabic-Turkish example sentences
Legal notices
Dictionaries
The textual content of classic dictionaries is mainly based on the two resources cited below. Changes (adding and removing vowels for Arabic words and phrases, changing translations, adding or changing grammatical categories) have been made to the original resources.
Resource name: Wiktionary . Licence: CC BY-SA 3.0. References: https://www.wiktionary.org.
Resource name: Buckwalter Arabic Morphological Analyzer Version 1.0. Licence: GNU General Public License v2. References: https://catalog.ldc.upenn.edu/LDC2002L49 ; Buckwalter, Tim. Buckwalter Arabic Morphological Analyzer Version 1.0 LDC2002L49. Web Download. Philadelphia: Linguistic Data Consortium, 2002.
Contextual translations
Contextual translations and example sentences are extracted from the following corpora:
Resource name: UNPC. Licence: not specified. References: https://conferences.unite.un.org/UNCorpus ; Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel Corpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016.
Resource name: OpenSubtitles. Licence: not specified. References: https://www.opensubtitles.org/ ; P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles.
Resource name: News-Commentary v14. Licence: not specified. References: J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) ; https://data.statmt.org/news-commentary/v14/.
Resource name: MultiUN. Licence: not specified. References: MultiUN: A Multilingual corpus from United Nation Documents, Andreas Eisele and Yu Chen, LREC 2010 ; https://www.euromatrixplus.net/multi-un.
Resource name: TED2020 v1. Licence: CC BY-NC-ND 4.0 International. Considerations: tarjamaan.com acknowledges the authorship of TED talks (BY) and does not redistribute transcripts for commercial purposes (NC) and preserves the original contents (ND) retrieved from https://opus.nlpl.eu/TED2020-v1.php. References: Reimers, Nils and Gurevych, Iryna: Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation.
Wikipedia
Resource name: Wikipedia. Licence: CC BY-SA 3.0. References: https://www.wikipedia.org.