Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms

• Published in: Information processing & management Vol. 56; no. 1; pp. 212 - 227
• Main Authors: Al-Salemi, Bassam, Ayob, Masri, Kendall, Graham, Noah, Shahrul Azman Mohd
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.01.2019; Elsevier Science Ltd
• Subjects: Adaptation; Algorithms; Arabic language; Arabic text categorization; Benchmarks; Classification; Datasets; Learning; Machine learning; Multi-label benchmark; Multi-label learning; RTAnews; Support vector machines; Text categorization; Transformations; Multi-label learning; Arabic text categorization; Multi-label benchmark; RTAnews
• ISSN: 0306-4573; 1873-5371; 1873-5371
• DOI: 10.1016/j.ipm.2018.09.008

Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms.