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Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition
2021
Computational Linguistics
This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech. The article then explores the idea that the difference between simple grammars exhibited by child learners and fully recursive grammars exhibited by adult learners may be an effect of increasing working memory
doi:10.1162/coli_a_00399
dblp:journals/coling/JinSDMS21
fatcat:h7plwbgjxjczbe3ykqmixikuya