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The semantic relations between Arabic word representations were recognized and widely studied in theoretical studies in linguistics many centuries ago. Nonetheless, most of the previous research in automatic information retrieval (IR) focused on stem or root-based indexing, while lemmas and patterns are under-exploited. However, the authors believe that each of the four morphological levels encapsulates a part of the meaning of words. That is, the purpose is to aggregate these levels using more sophisticated approaches to reach the optimal combination which enhances IR.

The authors first compare the state-of-the art Arabic natural language processing (NLP) tools in IR. This allows to select the most accurate tool in each representation level i.e. developing four basic IR systems. Then, the authors compare two rank aggregation approaches which combine the results of these systems. The first approach is based on linear combination, while the second exploits classification-based meta-search.

Combining different word representation levels, consistently and significantly enhances IR results. The proposed classification-based approach outperforms linear combination and all the basic systems.

The work stands by a standard experimental comparative study which assesses several NLP tools and combining approaches on different test collections and IR models. Thus, it may be helpful for future research works to choose the most suitable tools and develop more sophisticated methods for handling the complexity of Arabic language.

The originality of the idea is to consider that the richness of Arabic is an exploitable characteristic and no more a challenging limit. Thus, the authors combine 4 different morphological levels for the first time in Arabic IR. This approach widely overtook previous research results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0515