Output details
11 - Computer Science and Informatics
University of Edinburgh
Article title
A Bayesian framework for word segmentation: Exploring the effects of context
Type
D - Journal article
Title of journal
Cognition
Article number
-
Volume number
112
Issue number
1
First page of article
21
ISSN of journal
0010-0277
Year of publication
2009
Number of additional authors
2
Additional information
<22> Originality: Models presented in this paper pioneered the use of nonparametric Bayesian methods for unsupervised learning of linguistic structure.
Significance: Becoming a standard reference for work on word segmentation (Daland and Pierrehumbert 2011, Hewlett 2011). Both new models (Neubig et al. 2010, Jones et al. 2010) and new inference methods (Pearl et al. 2010, Liang et al. 2010, Mochihashi et al. 2009) are based on this work. Goldwater received an EPSRC grant to extend the models.
Rigour: Cited in Charniak's 2011 ACL Lifetime Achievement Award speech as an example of the kind of research the community should strive for.
Interdisciplinary
Yes
Cross-referral requested
-
Research group
D - Institute for Language, Cognition & Computation
Citation count
68
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
