Learning to Identify Semitic Roots

The standard account of word-formation processes in Semitic languages describes words as combinations of two morphemes: a root and a pattern. The root consists of consonants only, by default three (although longer roots are known), called radicals. The pattern is a combination of vowels and, possibly, consonants too, with ‘slots’ into which the root consonants can be inserted. Words are created by interdigitating roots into patterns: the first radical is inserted into the first consonantal slot of the pattern, the second radical fills the second slot and the third fills the last slot. Identifying the root of a given word is an important task. Although existing morphological analyzers for Hebrew only provide a lexeme (which is a combination of a root and a pattern), for other Semitic languages, notably Arabic, the root is an essential part of any morphological analysis simply because traditional dictionaries are organized by root, rather than by lexeme. Furthermore, roots are known to carry some meaning, albeit vague. We believe that this information can be useful for computational applications. We present a machine learning approach, augmented by limited linguistic knowledge, to the problem of identifying the roots of Semitic words. To the best of our knowledge, this is the first application of machine learning to this problem. While there exist programs which can extract the root of words in Arabic and Hebrew, they are all dependent on labor intensive construction of large-scale lexicons which are components of full-scale morphological analyzers. The challenge of our work is to automate this process, avoiding the bottleneck of having to laboriously list the root and pattern of each lexeme in the language, and thereby gain insights that can be used for more detailed morphological analysis of Semitic languages.