Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
A second-order Hidden Markov Model for part-of-speech tagging
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Factored language models and generalized parallel backoff
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Part-of-speech tagging based on hidden Markov model assuming joint independence
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Conditional structure versus conditional estimation in NLP models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Part of speech tagging in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
On the use of virtual evidence in conditional random fields
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
A multi-domain web-based algorithm for POS tagging of unknown words
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Predicting nucleosome positioning using multiple evidence tracks
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
| Hi-index | 0.00 |
We present a part-of-speech tagger which introduces two new concepts: virtual evidence in the form of an "observed child" node, and negative training data to learn the conditional probabilities for the observed child. Associated with each word is a flexible feature-set which can include binary flags, neighboring words, etc. The conditional probability of Tag given Word + Features is implemented using a factored language-model with back-off to avoid data sparsity problems. This model remains within the framework of Dynamic Bayesian Networks (DBNs) and is conditionally-structured, but resolves the label bias problem inherent in the conditional Markov model (CMM).