A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
An unsupervised morpheme-based HMM for hebrew morphological disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A hybrid Markov/semi-Markov conditional random field for sequence segmentation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
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
Hierarchical Bayesian domain adaptation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Nested named entity recognition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Joint parsing and semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Active learning with multiple annotations for comparable data classification task
BUCC '11 Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web
Minimally supervised domain-adaptive parse reranking for relation extraction
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
LODifier: generating linked data from unstructured text
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Extracting multilingual natural-language patterns for RDF predicates
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
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One of the main obstacles to producing high quality joint models is the lack of jointly annotated data. Joint modeling of multiple natural language processing tasks outperforms single-task models learned from the same data, but still under-performs compared to single-task models learned on the more abundant quantities of available single-task annotated data. In this paper we present a novel model which makes use of additional single-task annotated data to improve the performance of a joint model. Our model utilizes a hierarchical prior to link the feature weights for shared features in several single-task models and the joint model. Experiments on joint parsing and named entity recognition, using the OntoNotes corpus, show that our hierarchical joint model can produce substantial gains over a joint model trained on only the jointly annotated data.