  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.
