Towards a model of prediction-based syntactic category acquisition : first steps with word embeddings
Faculty of Arts. Linguistics and Literature
Lisbon :Association for Computational Linguistics, 2015
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning / Berwick, Robert [edit.]; et al.
University of Antwerp
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