TINA: A Probabilistic Syntactic Parser for Speech Understanding Systems
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1989
- Accession Number
- ADA458586
Entities
People
- Stephanie Seneff
Organizations
- Massachusetts Institute of Technology