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)
|
|
|
|
| |
|