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.