Distributional learning of parallel multiple context-free grammars
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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.
We present a learning algorithm for a large subclass of these grammars, that includes all regular languages but not all context-free languages. This algorithm relies on a generalisation of the notion of distribution as a function from tuples of strings to entire sentences; we define nonterminals using finite sets of these functions. Our learning algorithm uses a nonprobabilistic learning paradigm which allows for membership queries as well as positive samples; it runs in polynomial time.
KeywordsMildly context-sensitive Grammatical inference Semilinearity
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