An exploratory application of constraint optimization in mozart to probabilistic natural language processing

Authors:
Irene Langkilde-Geary
Affiliations:
Brigham Young University, Provo, UT
Venue:
CSLP'04 Proceedings of the First international conference on Constraint Solving and Language Processing
Year:
2004

Citing 3
Cited 2

Architectures for Natural Language Generation: Problems and Perspectives

EWNLG '93 Selected papers from the Fourth European Workshop on Trends in Natural Language Generation, An Artificial Intelligence Perspective
Building a large annotated corpus of English: the penn treebank

Computational Linguistics - Special issue on using large corpora: II
The Penn Treebank: annotating predicate argument structure

HLT '94 Proceedings of the workshop on Human Language Technology

Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models

Natural Language Engineering
A constraint programming approach to probabilistic syntactic processing

ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes an exploratory implementation in Mozart applying constraint optimization to basic subproblems of parsing and generation. Optimization is performed on the probability of a sentence using a dependency-style syntactic representation, which is computed using an adaptation of the English Penn Treebank as data. The same program solves both parsing and generation subproblems, providing the flexibility of a general architecture combined with practical efficiency. We show results on a sample sentence that is a classic in natural language processing.