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dc.contributor.advisorGoldwater, Sharon
dc.contributor.advisorKing, Simon
dc.contributor.authorPate, John Kenton
dc.date.accessioned2013-12-09T15:51:08Z
dc.date.available2013-12-09T15:51:08Z
dc.date.issued2013-07-02
dc.identifier.urihttp://hdl.handle.net/1842/8235
dc.description.abstractHuman language has two fundamental requirements: it must allow competent speakers to exchange messages efficiently, and it must be readily learned by children. Recent work has examined effects of language predictability on language production, with many researchers arguing that so-called “predictability effects” function towards the efficiency requirement. Specifically, recent work has found that talkers tend to reduce linguistic forms that are more probable more heavily. This dissertation proposes the “Predictability Bootstrapping Hypothesis” that predictability effects also make language more learnable. There is a great deal of evidence that the adult grammars have substantial statistical components. Since predictability effects result in heavier reduction for more probable words and hidden structure, they provide infants with direct cues to the statistical components of the grammars they are trying to learn. The corpus studies and computational modeling experiments in this dissertation show that predictability effects could be a substantial source of information to language-learning infants, focusing on the potential utility of phonetic reduction in terms of word duration for syntax acquisition. First, corpora of spontaneous adult-directed and child-directed speech (ADS and CDS, respectively) are compared to verify that predictability effects actually exist in CDS. While revealing some differences, mixed effects regressions on those corpora indicate that predictability effects in CDS are largely similar (in kind and magnitude) to predictability effects in ADS. This result indicates that predictability effects are available to infants, however useful they may be. Second, this dissertation builds probabilistic, unsupervised, and lexicalized models for learning about syntax from words and durational cues. One series of models is based on Hidden Markov Models and learns shallow constituency structure, while the other series is based on the Dependency Model with Valence and learns dependency structure. These models are then used to measure how useful durational cues are for syntax acquisition, and to what extent their utility in this task can be attributed to effects of syntactic predictability on word duration. As part of this investigation, these models are also used to explore the venerable “Prosodic Bootstrapping Hypothesis” that prosodic structure, which is cued in part by word duration, may be useful for syntax acquisition. The empirical evaluations of these models provide evidence that effects of syntactic predictability on word duration are easier to discover and exploit than effects of prosodic structure, and that even gold-standard annotations of prosodic structure provide at most a relatively small improvement in parsing performance over raw word duration. Taken together, this work indicates that predictability effects provide useful information about syntax to infants, showing that the Predictability Bootstrapping Hypothesis for syntax acquisition is computationally plausible and motivating future behavioural investigation. Additionally, as talkers consider the probability of many different aspects of linguistic structure when reducing according to predictability effects, this result also motivates investigation of Predictability Bootstrapping of other aspects of linguistic knowledge.en_US
dc.contributor.sponsorScottish Overseas Research Student Awards Scheme (SORSAS)en_US
dc.contributor.sponsorScottish Informatics and Computer Science Alliance (SICSA)en_US
dc.language.isoenen_US
dc.publisherThe University of Edinburghen_US
dc.relation.hasversionPate, J. K. and Goldwater, S. (2011). Predictability effects in infant-directed and adult-directed speech: Does the listener matter? In Proceedings of the 33rd Annual Conference of the Cognitive Science Society.en_US
dc.relation.hasversionPate, J. K. and Goldwater, S. (2011). Unsupervised syntactic chunking with acoustic cues: computational models for prosodic bootstrapping. In Proceedings of the 2nd ACL workshop on Cognitive Modeling and Computational Linguistics.en_US
dc.relation.hasversionPate, J. K. and Goldwater, S. (2013). Unsupervised dependency parsing with acoustic cues. Transactions of the ACL.en_US
dc.subjectlanguageen_US
dc.subjectpredictability effectsen_US
dc.subjectlanguage-learningen_US
dc.subjectadult-directed speechen_US
dc.subjectchild-directed speechen_US
dc.titlePredictability effects in language acquisitionen_US
dc.typeThesis or Dissertationen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US


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