Implicit Schemata and Categories in Memory-based Language Processing

Abstract

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.