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Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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Two models of minimalist, incremental syntactic analysis.

Edward P Stabler1

  • 1Department of Linguistics, UCLA, Los Angeles, CA 90095-1543, USA. stabler@ucla.edu

Topics in Cognitive Science
|June 13, 2013
PubMed
Summary

Minimalist grammars (MGs) offer exponential succinctness over multiple context-free grammars (MCFGs) for mildly context-sensitive languages. An incremental parser for MGs demonstrates this efficiency and integrates probabilistic influences early in analysis.

Keywords:
GrammarMinimalist grammarMultiple context-free grammarParsingSuccinctness

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Area of Science:

  • Computational Linguistics
  • Formal Language Theory
  • Parsing Algorithms

Background:

  • Minimalist grammars (MGs) and multiple context-free grammars (MCFGs) are weakly equivalent, defining the same class of mildly context-sensitive languages.
  • MGs offer greater succinctness due to generalized structure-building rules compared to MCFGs.
  • This succinctness impacts the efficiency of parsing models.

Purpose of the Study:

  • To define an incremental, top-down beam parser for Minimalist Grammars.
  • To demonstrate the parser's soundness and completeness for all MGs and MCFG languages.
  • To highlight the representational efficiency of MGs in parsing.

Main Methods:

  • Development of an incremental, top-down beam parsing algorithm specifically for MGs.
  • Ensuring the parser is sound and complete for the class of languages generated by MGs.
  • Transparent representation of the grammar within the parser to showcase succinctness.

Main Results:

  • The parser successfully parses all languages generated by MGs, including those from MCFGs.
  • The parser's design transparently reflects the succinctness advantage of MGs over MCFGs.
  • Probabilistic influences from broad domains can affect early parsing steps in incremental models.

Conclusions:

  • MGs provide a more succinct grammatical framework than MCFGs for mildly context-sensitive languages.
  • The developed incremental parser effectively leverages MG succinctness for efficient parsing.
  • The parser architecture supports early integration of probabilistic information, aligning with incremental processing expectations.