TINA. A probabilistic syntactic parser for speech understanding systems

A natural language system, TINA, which integrates key ideas from context-free grammars, augmented transition networks (ATNs), and unification grammars has been developed. The parser uses a best-first search strategy, with probability assignments on all arcs obtained automatically from a set of example sentences. An initial context-free grammar, derived from the example sentences, is converted to an implicit probabilistic network structure. Control includes both top-down and bottom-up cycles, and key parameters are passed among nodes to deal with long-distance movement and agreement constraints. The probabilities provide a natural mechanism for exploring more common grammatical constructions first. Arc probabilities reduce test-set perplexity sixfold. A strategy for dealing with movement that can handle nested and chained gaps efficiently and rejects crossed gaps is introduced