Redington, M. & Chater, N. (1997). Probabilistic and distributional
approaches to language acquisition. Trends in Cognitive
Sciences, 1 (7), 273-281.
Abstract
Recent computational research on natural language corpora has revealed
that relatively simple statistical learning mechanisms could make an
important contribution to certain aspects of language acquisition.
For example, statistical and connectionist methods can
provide valuable cues to word segmentation, and to the acquisition of
inflectional morphology, syntactic classes, and aspects of word
meaning. In each case, these cues are partial, and must be integrated
with additional information, whether from other environmental cues or
innate knowledge, to provide a complete solution to the acquisition
problem. The success of these methods with real natural language
corpora demonstrates their feasibility as part of the language
acquisition mechanism, where much previous research has been limited
to highly idealized artificial input or to a priori considerations
regarding the feasibility of acquisition mechanisms. Exploring
probabilistic learning mechanisms with natural language input provides
both an empirical basis for assessing how innate constraints interact with
information derived from the environment, and a source of
hypotheses for experimental test.
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Last modified: Jan 10, 1998