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        <title>Probabilistic Parsing for Spoken Language Applications</title>
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    <name type="personal">
        <namePart type="given">Stephanie</namePart>
        <namePart type="family">Seneff</namePart>
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            <title>Proceedings of the First International Workshop on Parsing Technologies</title>
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        <name type="personal">
            <namePart type="given">Masaru</namePart>
            <namePart type="family">Tomita</namePart>
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            <publisher>Carnegy Mellon University</publisher>
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    <abstract>A new natural language system, TINA, has been developed for applications involving spoken language tasks, which integrate key ideas from context free grammars, Augmented Transition Networks (ATN’s) [6], and Lexical Functional Grammars (LFG’s) [1]. The parser uses a best-first 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, agreement, and semantic constraints. The probabilities provide a natural mechanism for exploring more common grammatical constructions first. One novel feature of TINA is that it provides an atuomatic sentence generation capability, which has been very effective for identifying overgeneration problems. A fully integrated spoken language system using this parser is under development.</abstract>
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        <date>1989-08</date>
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%0 Conference Proceedings
%T Probabilistic Parsing for Spoken Language Applications
%A Seneff, Stephanie
%Y Tomita, Masaru
%S Proceedings of the First International Workshop on Parsing Technologies
%D 1989
%8 August
%I Carnegy Mellon University
%C Pittsburgh, Pennsylvania, USA
%F seneff-1989-probabilistic
%X A new natural language system, TINA, has been developed for applications involving spoken language tasks, which integrate key ideas from context free grammars, Augmented Transition Networks (ATN’s) [6], and Lexical Functional Grammars (LFG’s) [1]. The parser uses a best-first 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, agreement, and semantic constraints. The probabilities provide a natural mechanism for exploring more common grammatical constructions first. One novel feature of TINA is that it provides an atuomatic sentence generation capability, which has been very effective for identifying overgeneration problems. A fully integrated spoken language system using this parser is under development.
%U https://aclanthology.org/W89-0222
%P 209-218

Markdown (Informal)

[Probabilistic Parsing for Spoken Language Applications](https://aclanthology.org/W89-0222) (Seneff, IWPT 1989)

• Probabilistic Parsing for Spoken Language Applications (Seneff, IWPT 1989)

ACL

• Stephanie Seneff. 1989. Probabilistic Parsing for Spoken Language Applications. In Proceedings of the First International Workshop on Parsing Technologies, pages 209–218, Pittsburgh, Pennsylvania, USA. Carnegy Mellon University.