Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Connectionist learning of belief networks
Artificial Intelligence
An introduction to variational methods for graphical models
Learning in graphical models
A mean field learning algorithm for unsupervised neural 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
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Reinforcement learning for factored markov decision processes
Reinforcement learning for factored markov decision processes
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
PCFG models of linguistic tree representations
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Efficient probabilistic top-down and left-corner parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Inducing history representations for broad coverage statistical parsing
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Intricacies of Collins' Parsing Model
Computational Linguistics
Discriminative training of a neural network statistical parser
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Advances in discriminative parsing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Hidden-variable models for discriminative reranking
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Unlexicalised hidden variable models of split dependency grammars
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
A latent variable model of synchronous parsing for syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A latent variable model of synchronous syntactic-semantic parsing for multiple languages
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Loss minimization in parse reranking
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Dependency parsing with dynamic Bayesian network
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
A latent variable model for generative dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Head-driven PCFGs with latent-head statistics
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Constituent parsing by classification
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
The Journal of Machine Learning Research
Bayesian network automata for modelling unbounded structures
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
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We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output structure. These incremental sigmoid belief networks (ISBNs) make decoding possible because inference with partial output structures does not require summing over the unboundedly many compatible model structures, due to their directed edges and incrementally specified model structure. ISBNs are specifically targeted at challenging structured prediction problems such as natural language parsing, where learning the domain's complex statistical dependencies benefits from large numbers of latent variables. While exact inference in ISBNs with large numbers of latent variables is not tractable, we propose two efficient approximations. First, we demonstrate that a previous neural network parsing model can be viewed as a coarse mean-field approximation to inference with ISBNs. We then derive a more accurate but still tractable variational approximation, which proves effective in artificial experiments. We compare the effectiveness of these models on a benchmark natural language parsing task, where they achieve accuracy competitive with the state-of-the-art. The model which is a closer approximation to an ISBN has better parsing accuracy, suggesting that ISBNs are an appropriate abstract model of natural language grammar learning.