10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Falsifying serial and parallel parsing models: empirical conundrums and an overlooked paradigm.

      1
      Journal of psycholinguistic research
      Springer Science and Business Media LLC

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          When the human parser encounters a local structural ambiguity, are multiple structures pursued (parallel or breadth-first parsing), or just a single preferred structure (serial or depth-first parsing)? This note discusses four important classes of serial and parallel models: simple limited parallel, ranked limited parallel, deterministic serial with reanalysis, and probabilistic serial with reanalysis. It is argued that existing evidence is compatible only with probabilistic serial-reanalysis models, or ranked parallel models augmented with a reanalysis component. A new class of linguistic structures is introduced on which the behavior of serial and parallel parsers diverge the most radically: multiple local ambiguities are stacked to increase the number of viable alternatives in the ambiguous region from two to eight structures. This paradigm may provide the strongest test yet for parallel models.

          Related collections

          Author and article information

          Journal
          J Psycholinguist Res
          Journal of psycholinguistic research
          Springer Science and Business Media LLC
          0090-6905
          0090-6905
          Mar 2000
          : 29
          : 2
          Affiliations
          [1 ] Department of Computer and Information Science, Ohio State University, Columbus 43210, USA. rick@cis.ohio-state.edu
          Article
          10.1023/a:1005105414238
          10709188
          1200facb-e997-4302-a32e-595d75895e71
          History

          Comments

          Comment on this article