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BEYOND STATISTICAL LEARNING IN SYNTAX



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BEYOND S TATISTICAL LEARNING IN SYNTAX ERI TAKAHASHI AND JEFFREY LIDZ 1 Introduction Knowing the structural representation of sentences is a fundamental step for acquiring a language However the input to a child does not come with obvious labels to signal constituency it seems like simple linear sequences of words Since both words and constituent structure vary from language to language children have to learn how words go together to form constituents in the particular language they are learning Therefore some learning mechanism must be present that guides the learner to build the correct phrase structure What kind of input is necessary and what kind of information is used by children to come to the correct representation It is most likely that children employ various kinds of information to arrive at the correct phrase structure representation perhaps a combination of cues from prosody function words agreement morphology semantics and distribution This paper will focus on distributional cues to phrase structure Recent studies in artificial language learning have shown that distributional information can play a role in the acquisition of phonemes Maye Werker Gerken 2002 Maye Gerken 2000 word segmentation Saffran Aslin Newport 1996 word categories Mintz 2003 and syntax like regularities Gomez Gerken 1999 In particular it has been proposed that transitional probabilities which is a statistic that measures the predictiveness of the following element given a previous element can be used by learners to successfully learn phrasal groupings of words Thompson Newport 2007 in miniature artificial languages Thompson Newport 2007 showed that the adult subjects successfully learned the phrasal groupings of an artificial language based on the transitional probabilities However the artificial grammar in Thompson Newport 2007 contained phrases with no internal structure and consequently leaves open the question of whether statistical cues to multiply embedded hierarchical



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