  A theory of language learning is described, which uses Bayesian induction of
feature structures (scripts) and script functions. Each word sense in a
language is mentally represented by an m-script, a script function which
embodies all the syntax and semantics of the word. M-scripts form a
fully-lexicalised unification grammar, which can support adult language. Each
word m-script can be learnt robustly from about six learning examples. The
theory has been implemented as a computer model, which can bootstrap-learn a
language from zero vocabulary. The Bayesian learning mechanism is (1) Capable:
to learn arbitrarily complex meanings and syntactic structures; (2) Fast:
learning these structures from a few examples each; (3) Robust: learning in the
presence of much irrelevant noise, and (4) Self-repairing: able to acquire
implicit negative evidence, using it to learn exceptions. Children learning
language are clearly all of (1) - (4), whereas connectionist theories fail on
(1) and (2), and symbolic theories fail on (3) and (4). The theory is in good
agreement with many key facts of language acquisition, including facts which
are problematic for other theories. It is compared with over 100 key
cross-linguistic findings about acquisition of the lexicon, phrase structure,
morphology, complementation and control, auxiliaries, verb argument structures,
gaps and movement - in nearly all cases giving unforced agreement without extra
assumptions.
