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Abstract
We propose an explicit, incremental strategy by which children could group words with
similar syntactic privileges into discrete, unlabeled categories. This strategy, which
can discover lexical ambiguity, is based in part on a generalization of the idea of
sentential minimal pairs. As a result, it makes minimal assumptions about the availability
of syntactic knowledge at the onset of categorization. Although the proposed strategy
is distributional, it can make use of categorization cues from other domains, including
semantics and phonology. Computer simulations show that this strategy is effective
at categorizing words in both artificial-language samples and transcripts of naturally-occurring,
child-directed speech. Further, the simulations show that the proposed strategy performs
even better when supplied with semantic information about concrete nouns. Implications
for theories of categorization are discussed.