Advances in the computational study of language acquisition.
This paper provides a tutorial introduction to computational studies of how children learn their native languages. Its aim is to make recent advances accessible to the broader research community, and to place them in the context of current theoretical issues. The first section locates computational studies and behavioral studies within a common theoretical framework. The next two sections review two papers that appear in this volume: one on learning the meanings of words and one or learning the sounds of words. The following section highlights an idea which emerges independently in these two papers and which I have dubbed autonomous bootstrapping. Classical bootstrapping hypotheses propose that children begin to get a toc-hold in a particular linguistic domain, such as syntax, by exploiting information from another domain, such as semantics. Autonomous bootstrapping complements the cross-domain acquisition strategies of classical bootstrapping with strategies that apply within a single domain. Autonomous bootstrapping strategies work by representing partial and/or uncertain linguistic knowledge and using it to analyze the input. The next two sections review two more more contributions to this special issue: one on learning word meanings via selectional preferences and one on algorithms for setting grammatical parameters. The final section suggests directions for future research.
Brent
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Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21218, USA. brent@jhu.edu
D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D006801:Humans; D007223:Infant; D007804:Language Development; D008390:Markov Chains; D010700:Phonetics; D011578:Psycholinguistics; D012660:Semantics; D014825:Vocabulary