TINA: a probabilistic syntactic parser for speech understanding systems

  • Authors:
  • Stephanie Seneff

  • Affiliations:
  • -

  • Venue:
  • HLT '89 Proceedings of the workshop on Speech and Natural Language
  • Year:
  • 1989

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Abstract

A new natural language system, TINA, has been developed for applications involving speech understanding tasks, which integrates key ideas from context free grammars, Augmented Transition Networks (ATN's) [1], and Lexical Functional Grammars (LFG's) [2]. 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 first converted to a 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 also reduced test-set perplexity by nearly an order of magnitude. Included is a new strategy for dealing with movement, which can handle efficiently nested and chained gaps, and rejects crossed gaps.