Islamic Fatwa Request Routing via Hierarchical Multi-label Arabic Text Categorization

Multi-label classification (MLC) is concerned withlearning from examples where each example is associatedwith a set of labels in opposite to traditional single-labelclassification where an example typically is assigned a single label. MLC problems appear in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. The religious domain has become an interesting and challenging area for machine learning and natural language processing. A "fatwa" in the Islamic religion represents the legal opinion or interpretation that a qualified scholar (mufti) can give on issues related to the Islamic law. It is similar to the issue of legal opinions from courts in common-law systems. In this paper, a hierarchical classification system is introduced to automatically route incoming fatwa requests to the most relevant mufti. Each fatwa is associated to multiple categories by mufti where the categories can be organized in a hierarchy. The results on fatwa requests routing have confirmed the effective and efficient predictive performance of hierarchical ensembles of multi-label classifiers trained using the HOMER method and its variations compared to binary relevance which simply trains a classifier for each label independently.