Publication
Title
Towards a model of prediction-based syntactic category acquisition : first steps with word embeddings
Author
Abstract
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
Language
English
Source (book)
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning / Berwick, Robert [edit.]; et al.
Publication
Lisbon : Association for Computational Linguistics, 2015
ISBN
978-1-941643-32-7
Volume/pages
p. 28-32
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 08.01.2016
Last edited 09.01.2016
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