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Bootstrapping the Syntactic Bootstrapper : Probabilistic Labeling of Prosodic Phrases
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In: Language Acquisition ; 22 (2015), 3. - S. 285-309. - ISSN 1048-9223. - eISSN 1532-7817 (2015)
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Cross-situational word learning in the right situations.
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In: ISSN: 0278-7393 ; EISSN: 1939-1285 ; Journal of Experimental Psychology: Learning, Memory, and Cognition ; https://hal.archives-ouvertes.fr/hal-02473029 ; Journal of Experimental Psychology: Learning, Memory, and Cognition, American Psychological Association, 2014, 40 (3), pp.892-903. ⟨10.1037/a0035657⟩ (2014)
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Unsupervised word segmentation in context
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Abstract:
This paper extends existing word segmentation models to take non-linguistic context into account. It improves the token F-score of a top performing segmentation models by 2.5% on a 27k utterances dataset. We posit that word segmentation is easier in-context because the learner is not trying to access irrelevant lexical items. We use topics from a Latent Dirichlet Allocation model as a proxy for "activities" contexts, to label the Providence corpus. We present Adaptor Grammar models that use these context labels, and we study their performance with and without context annotations at test time. ; 9 page(s)
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URL: http://hdl.handle.net/1959.14/336964
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Phrasal prosody constrains online syntactic analysis in two-year-old children ...
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Toddlers Default to Canonical Surface-to-Meaning Mapping When Learning Verbs
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