A Trellis-based algorithm for estimating the parameters of a hidden stochastic context-free grammar
HLT '91 Proceedings of the workshop on Speech and Natural Language
Directed hypergraphs and applications
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Parsing of context-free languages
Handbook of formal languages, vol. 2
An Improved Context-Free Recognizer
ACM Transactions on Programming Languages and Systems (TOPLAS)
PROLOG and Natural Language Analysis
PROLOG and Natural Language Analysis
Parsing inside-out
Finite-state transducers in language and speech processing
Computational Linguistics
New figures of merit for best-first probabilistic chart parsing
Computational Linguistics
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Dynamic Programming Algorithms as Products of Weighted Logic Programs
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Inducing synchronous grammars with slice sampling
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Products of weighted logic programs
Theory and Practice of Logic Programming
Vine pruning for efficient multi-pass dependency parsing
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Hi-index | 0.00 |
While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis, which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra's algorithm can be used to construct a probabilistic chart parser with an O(n3) time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as is allowed by the inherent ordering of probabilistic dependencies.