Probabilistic and distributional approaches to language acquisition

Abstract

Recent computational research on natural language corpora has revealed that relatively simple statistical learning mechanisms can 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, an area where previously most 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 testing.