Ibn-Ginni: An Improved Morphological Analyzer for Arabic

Arabic is a morphologically rich language, which means that the Arabic language has a complicated system of word formation and structure. The affixes in the Arabic language (i.e., prefixes and suffixes) can be added to root words to generate different meanings and grammatical functions. These affixes can indicate aspects such as tense, gender, number, case, person, and more. In addition, the meaning and function of words can be modified in Arabic using an internal structure known as morphological patterns. Computational morphological analyzers of Arabic are vital to developing Arabic language processing toolkits. In this article, we introduce a new morphological analyzer (Ibn-Ginni) that inherits the speed and quality of the Buckwalter Arabic Morphological Analyzer (BAMA). The BAMA has poor coverage of the classical Arabic language. Hence, the coverage of classical Arabic is improved by using the Alkhalil analyzer. Although it is slow, it was used to generate a huge number of solutions for 3 million unique Arabic words collected from different resources. These word form-based solutions were converted to stem-based solutions, refined manually, and added to the database of BAMA, resulting in substantial improvements in the quality of the analysis. Hence, Ibn-Ginni is a hybrid system between BAMA and Alkhalil analyzers and may be considered an efficient large-scale analyzer. The Ibn-Ginni analyzer analyzed 0.6 million more words than the BAMA analyzer. Therefore, our analyzer significantly improves the coverage of the Arabic language. Besides, the Ibn-Ginni analyzer is high speed at providing solutions, the average time to analyze a word is 0.3 ms. Using a corpus designed for benchmarking Arabic morphological analyzers, our analyzer was able to find all solutions for 72.72% of the words. Moreover, the analyzer did not provide all possible morphological solutions for 24.24% of the words. The analyzer and its morphological database are publicly available on GitHub.