2017/02/07 by Tarek Sakakini, Sakakini, Tarek, Suma Bhat +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1702.02211
openalex publication_date 2017/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present an unsupervised and language-agnostic method for learning root-and-pattern morphology in Semitic languages. This form of morphology, abundant in Semitic languages, has not been handled in prior unsupervised approaches. We harness the syntactico-semantic information in distributed word representations to solve the long standing problem of root-and-pattern discovery in Semitic languages. Moreover, we construct an unsupervised root extractor based on the learned rules. We prove the validity of learned rules across Arabic, Hebrew, and Amharic, alongside showing that our root extractor compares favorably with a widely used, carefully engineered root extractor: ISRI.