Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences
نویسندگان
چکیده
منابع مشابه
The Acquisition of Word Meaning through Global Lexical Co-occurrences
The acquisition of word meaning has been extensively studied for the last thirty years in the field of language acquisition. However, the question of how children acquire word meaning remains highly controversial today. Recently, a number of computational studies have examined the emergence of lexical representations in connectionist networks or similar statistical systems, suggesting that word...
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In this study, results of computational simulations on English child-directed speech are presented to uncover what distributional properties of words make it easier to group them into lexical categories. This analysis provides evidence that words are easier to categorize when (i) they are hard to predict given the contexts they occur in; (ii) they occur in few different contexts; and (iii) thei...
متن کاملWhich distributional cues help the most? Unsupervised contexts selection for lexical category acquisition
Starting from the distributional bootstrapping hypothesis, we propose an unsupervised model that selects the most useful distributional information according to its salience in the input, incorporating psycholinguistic evidence. With a supervised Parts-of-Speech tagging experiment, we provide preliminary results suggesting that the distributional contexts extracted by our model yield similar pe...
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Psycholinguistic studies suggest that early on children acquire robust knowledge of the abstract lexical categories such as nouns, verbs and determiners (e.g., Gelman & Taylor, 1984; Kemp et al., 2005). Children’s grouping of words into categories might be based on various cues, including the phonological and morphological properties of a word, the distributional information about its surroundi...
متن کاملDistributed Latent Variable Models of Lexical Co-occurrences
Low-dimensional representations for lexical co-occurrence data have become increasingly important in alleviating the sparse data problem inherent in natural language processing tasks. This work presents a distributed latent variable model for inducing these low-dimensional representations. The model takes inspiration from both connectionist language models [1, 16] and latent variable models [13...
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ژورنال
عنوان ژورنال: PLOS ONE
سال: 2018
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0209449