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Title
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Towards a model of prediction-based syntactic category acquisition : first steps with word embeddings
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Author
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Abstract
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| We present a prototype model, based on a combination of count-based distributional semantics and prediction-based neural word embeddings, which learns about syntactic categories as a function of (1) writing contextual, phonological, and lexical-stress-related information to memory and (2) predicting upcoming context words based on memorized information. The system is a first step towards utilizing recently popular methods from Natural Language Processing for exploring the role of prediction in childrens acquisition of syntactic categories.1 |
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Language
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English
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Source (book)
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Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning / Berwick, Robert [edit.]; et al.
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Publication
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Lisbon
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Association for Computational Linguistics
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2015
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ISBN
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978-1-941643-32-7
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Volume/pages
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p. 28-32
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Full text (open access)
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