  In spite of its robust syntax, semantic cohesion, and less ambiguity, lemma
level analysis and generation does not yet focused in Arabic NLP literatures.
In the current research, we propose the first non-statistical accurate Arabic
lemmatizer algorithm that is suitable for information retrieval (IR) systems.
The proposed lemmatizer makes use of different Arabic language knowledge
resources to generate accurate lemma form and its relevant features that
support IR purposes. As a POS tagger, the experimental results show that, the
proposed algorithm achieves a maximum accuracy of 94.8%. For first seen
documents, an accuracy of 89.15% is achieved, compared to 76.7% of up to date
Stanford accurate Arabic model, for the same, dataset.
