Fixing the Infix: Unsupervised Discovery of Root-and-Pattern Morphology

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.