Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Building problem solvers
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Introduction in inference in Bayesian networks
Learning in graphical models
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Stochastic attribute-value grammars
Computational Linguistics
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Maximum entropy estimation for feature forests
HLT '02 Proceedings of the second international conference on Human Language Technology Research
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A model of syntactic disambiguation based on lexicalized grammars
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Log-linear models for wide-coverage CCG parsing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Probabilistic disambiguation models for wide-coverage HPSG parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Deep linguistic analysis for the accurate identification of predicate-argument relations
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Maximum entropy estimation for feature forests
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Feature forest models for probabilistic hpsg parsing
Computational Linguistics
Active learning and logarithmic opinion pools for hpsg parse selection
Natural Language Engineering
Deterministic shift-reduce parsing for unification-based grammars by using default unification
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Extremely lexicalized models for accurate and fast HPSG parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Efficiency in unification-based N-best parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
A log-linear model with an n-gram reference distribution for accurate HPSG parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Efficient extraction of grammatical relations
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Scalable discriminative parsing for German
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Improving the quality of text understanding by delaying ambiguity resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Computational linguistics and natural language processing
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
High efficiency realization for a wide-coverage unification grammar
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Adapting a probabilistic disambiguation model of an HPSG parser to a new domain
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Stochastic unification-based grammars (SUBGs) define exponential distributions over the parses generated by a unification-based grammar (UBG). Existing algorithms for parsing and estimation require the enumeration of all of the parses of a string in order to determine the most likely one, or in order to calculate the statistics needed to estimate a grammar from a training corpus. This paper describes a graph-based dynamic programming algorithm for calculating these statistics from the packed UBG parse representations of Maxwell and Kaplan (1995) which does not require enumerating all parses. Like many graphical algorithms, the dynamic programming algorithm's complexity is worst-case exponential, but is often polynomial. The key observation is that by using Maxwell and Kaplan packed representations, the required statistics can be rewritten as either the max or the sum of a product of functions. This is exactly the kind of problem which can be solved by dynamic programming over graphical models.