Foundations of statistical natural language processing
Foundations of statistical natural language processing
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Parsing inside-out
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Dynamic programming for parsing and estimation of stochastic unification-based grammars
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Effective statistical models for syntactic and semantic disambiguation
Effective statistical models for syntactic and semantic disambiguation
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning to recognize features of valid textual entailments
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A joint model for semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Generalized inference with multiple semantic role labeling systems
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Joint parsing and semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Piecewise pseudolikelihood for efficient training of conditional random fields
Proceedings of the 24th international conference on Machine learning
A global joint model for semantic role labeling
Computational Linguistics
Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Monte carlo inference and maximization for phrase-based translation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Learning with probabilistic features for improved pipeline models
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Active learning for pipeline models
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Joint parsing and named entity recognition
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Piecewise training for structured prediction
Machine Learning
A global model for joint lemmatization and part-of-speech prediction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Automatic diacritization for low-resource languages using a hybrid word and consonant CMM
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Convolution kernel over packed parse forest
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Hierarchical sequential learning for extracting opinions and their attributes
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
A probabilistic morphological analyzer for Syriac
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Improving the quality of text understanding by delaying ambiguity resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Monte Carlo techniques for phrase-based translation
Machine Translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Fine-grained class label markup of search queries
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Towards a top-down and bottom-up bidirectional approach to joint information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Exact sampling and decoding in high-order hidden Markov models
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Beyond myopic inference in big data pipelines
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Information extraction as a filtering task
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Joint inference of entities, relations, and coreference
Proceedings of the 2013 workshop on Automated knowledge base construction
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The end-to-end performance of natural language processing systems for compound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline architecture, which causes errors to propagate and compound at each stage. We present a novel architecture, which models these pipelines as Bayesian networks, with each low level task corresponding to a variable in the network, and then we perform approximate inference to find the best labeling. Our approach is extremely simple to apply but gains the benefits of sampling the entire distribution over labels at each stage in the pipeline. We apply our method to two tasks -- semantic role labeling and recognizing textual entailment -- and achieve useful performance gains from the superior pipeline architecture.