Distributional learning of parallel multiple context-free grammars

摘要

Natural languages require grammars beyond context-free for their description. Here we extend a family of distributional learning algorithms for context-free grammars to the class of Parallel Multiple Context-Free Grammars (pmcfgs). These grammars have two additional operations beyond the simple context-free operation of concatenation: the ability to interleave strings of symbols, and the ability to copy or duplicate strings. This allows the grammars to generate some non-semilinear languages, which are outside the class of mildly context-sensitive grammars. These grammars, if augmented with a suitable feature mechanism, are capable of representing all of the syntactic phenomena that have been claimed to exist in natural language.