Output details
11 - Computer Science and Informatics
University of Edinburgh
Article title
Covariance in Unsupervised Learning of Probabilistic Grammars
Type
D - Journal article
DOI
-
Title of journal
Journal of Machine Learning Research
Article number
-
Volume number
11
Issue number
Nov
First page of article
3017
ISSN of journal
1532-4435
Year of publication
2010
Number of additional authors
1
Additional information
<22> Originality: This paper makes novel first use of the Bayesian setting for the problem of grammar induction. The paper derives novel prior distributions for learning the syntax of language, and its results considerably improved the previously best experimental results in unsupervised parsing.
Significance: Solutions for the problem of grammar induction are long sought-after: they have implications both from the engineering perspective and the scientific perspective (for understanding how humans acquire language).
Rigour: The paper includes thorough experiments with six languages, and shows that the results generalize to these languages.
Interdisciplinary
-
Cross-referral requested
-
Research group
D - Institute for Language, Cognition & Computation
Citation count
6
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
