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- W2907954597 startingPage "e0209449" @default.
- W2907954597 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." @default.
- W2907954597 created "2019-01-11" @default.
- W2907954597 creator A5029255272 @default.
- W2907954597 creator A5055723498 @default.
- W2907954597 creator A5065590784 @default.
- W2907954597 creator A5083411784 @default.
- W2907954597 date "2018-12-28" @default.
- W2907954597 modified "2023-09-23" @default.
- W2907954597 title "Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences" @default.
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- W2907954597 doi "https://doi.org/10.1371/journal.pone.0209449" @default.
- W2907954597 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6310260" @default.
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- W2907954597 hasPublicationYear "2018" @default.
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