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Tailoring the Input to Children's Needs: The Use of Fine Lexical Tuning in Speech Directed to Normally Hearing Children and Children With Cochlear Implants
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In: Front Psychol (2021)
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Word characteristics and speech production accuracy in children with auditory brainstem implants: a longitudinal triple case report ...
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Word characteristics and speech production accuracy in children with auditory brainstem implants: a longitudinal triple case report ...
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Native and non-native listeners’ judgements on the overall speech quality of hearing-impaired children ...
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Native and non-native listeners’ judgements on the overall speech quality of hearing-impaired children ...
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What makes a distributional context useful? Lexical diversity is more important than frequency ...
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Children Probably Store Short Rather Than Frequent or Predictable Chunks: Quantitative Evidence From a Corpus Study
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Prosodic modulation in the babble of cochlear implanted and normally hearing infants: A perceptual study using a visual analogue scale ...
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Supplementary Material, supplemental_material_2_9 – Prosodic modulation in the babble of cochlear implanted and normally hearing infants: A perceptual study using a visual analogue scale ...
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Prosodic modulation in the babble of cochlear implanted and normally hearing infants: A perceptual study using a visual analogue scale ...
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Supplementary Material, supplemental_material_2_9 – Prosodic modulation in the babble of cochlear implanted and normally hearing infants: A perceptual study using a visual analogue scale ...
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Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences
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Abstract:
This paper analyzes distributional properties that facilitate the categorization of words into lexical categories. First, word-context co-occurrence counts were collected using corpora of transcribed English child-directed speech. Then, an unsupervised k-nearest neighbor algorithm was used to categorize words into lexical categories. The categorization outcome was regressed over three main distributional predictors computed for each word, including frequency, contextual diversity, and average conditional probability given all the co-occurring contexts. Results show that both contextual diversity and frequency have a positive effect while the average conditional probability has a negative effect. This indicates that words are easier to categorize in the face of uncertainty: categorization works best for words which are frequent, diverse, and hard to predict given the co-occurring contexts. This shows how, in order for the learner to see an opportunity to form a category, there needs to be a certain degree of uncertainty in the co-occurrence pattern.
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Keyword:
Research Article
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URL: https://doi.org/10.1371/journal.pone.0209449 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310260/ http://www.ncbi.nlm.nih.gov/pubmed/30592738
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Facilitatory Effects of Multi-Word Units in Lexical Processing and Word Learning: A Computational Investigation
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Noun plural production in preschoolers with early cochlear implantation: An experimental study of Dutch and German
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In: Internationale Zeitschrift für Fachsprachenforschung, -didaktik und Terminologie 79 (2015) 4, 561-569
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IDS Bibliografie zur deutschen Grammatik
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