Output details
11 - Computer Science and Informatics
University of Oxford
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Output 0 of 0 in the submission
Output title
A hierarchical Pitman-Yor process HMM for unsupervised part of speech induction
Type
E - Conference contribution
DOI
-
Name of conference/published proceedings
ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Volume number
1
Issue number
-
First page of article
865
ISSN of proceedings
-
Year of publication
2011
Number of additional authors
1
Additional information
<22>
This paper introduced non-parametric Bayesian modelling and inference techniques for the popular Hidden Markov Model. In particular it addresses the problem of learning the syntactic categories of words without any training data (unsupervised learning). The techniques presented were demonstrated to be more accurate than those previously published, and this was confirmed in an open competition at the PASCAL Challenge on Grammar Induction, NAACL2012. The research presented in this paper underpinned a successful EPSRC Fellowship application. ACL2009 had an acceptance rate of 26%.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
4
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
