Implicit Schemata and Categories in Memory-based Language Processing.

Memory-based language processing (MBLP) is an approach to language processing based on exemplar storage during learning and analogical reasoning during processing. From a cognitive perspective, the approach is attractive as a model for human language processing because it does not make any assumptio...

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Publicado en:Language & Speech Vol. 56; no. 3; pp. 309 - 329
Autores principales: van den Bosch, Antal, Daelemans, Walter
Formato: equations & formulas research tables/charts Journal Article
Publicado: Sage Publications Inc. Sep2013
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Implicit Schemata and Categories in Memory-based Language Processing.
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          van den Bosch, Antal
          Daelemans, Walter
      sug:
        subj:
          Memory
          Language Processing
          Theory Construction Classification
          Learning Methods
          Language Development
          Linguistics
          Human
          Analytic Research
          Comparative Studies
          Decision Making
          Concept Mapping
          Computer Simulation
          Cues
          Information Retrieval
          Hypothesis
      ab: Memory-based language processing (MBLP) is an approach to language processing based on exemplar storage during learning and analogical reasoning during processing. From a cognitive perspective, the approach is attractive as a model for human language processing because it does not make any assumptions about the way abstractions are shaped, nor any a priori distinction between regular and exceptional exemplars, allowing it to explain fluidity of linguistic categories, and both regularization and irregularization in processing. Schema-like behaviour and the emergence of categories can be explained in MBLP as by-products of analogical reasoning over exemplars in memory. We focus on the reliance of MBLP on local (versus global) estimation, which is a relatively poorly understood but unique characteristic that separates the memory-based approach from globally abstracting approaches in how the model deals with redundancy and parsimony. We compare our model to related analogy-based methods, as well as to example-based frameworks that assume some systemic form of abstraction.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
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        Journal Article
      ougenre: Article
    language: English
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