Output details
11 - Computer Science and Informatics
Aston University
Article title
Discriminative training of the hidden vector state model for semantic parsing
Type
D - Journal article
Title of journal
IEEE transactions on knowledge and data engineering
Article number
-
Volume number
21
Issue number
1
First page of article
66
ISSN of journal
1041-4347
Year of publication
2009
Number of additional authors
1
Additional information
<22> This paper explores a novel use of discriminative training for the Hidden Vector Space (HVS) model which overcomes the drawbacks of maximum likelihood estimation and improves HVS performance significantly. The work has influenced other people's research, such as I. Meza-Ruiz from the University of Edinburgh and F. Jurcícek from the University of West Bohemia in Pilsen, Czech Republic. It also led to a 3-year grant of RMB250,000 (approx. £25,000) funded by the Natural Science Foundation of China, "Open Information Extraction using Weakly-Supervised Learning".
Interdisciplinary
-
Cross-referral requested
-
Research group
B - Computer Science Research Group
Citation count
16
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
