Bootstrapping Language Acquisition
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 58755233
- Type
- D - Journal article
- DOI
-
10.1016/j.cognition.2017.02.009
- Title of journal
- Cognition
- Article number
- -
- First page
- 116
- Volume
- 164
- Issue
- -
- ISSN
- 0010-0277
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
D - Language, Interaction and Robotics
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first computational model of syntactic/semantic acquisition to be evaluated for specific behavioural effects after training on real child-directed language. The desired effects fall out from using a probabilistic joint learning model, without additional stipulations that have been claimed necessary. The work is discussed at length in the Oxford Handbook of Experimental Syntax (Sprouse, ed., in press; Oxford Handbooks are the premier review/reference work in Linguistics), and was mentioned as a "favourite recent paper" in Joshua Tenenbaum's ACL 2020 invited plenary. Data annotations created for this paper have been released; follow-on work is extending these to Hebrew for cross-linguistic evaluation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -