Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
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
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A Probabilistic Approach for Adapting Information Extraction Wrappers and Discovering New Attributes
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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
Hot Item Mining and Summarization from Multiple Auction Web Sites
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Conditional random fields is a probabilistic approach which has been applied to sequence labeling task achieving good performance. We attempt to extend the model so that human effort in preparing labeled training examples can be reduced by considering unlabeled data. Instead of maximizing the conditional likelihood, we aim at maximizing the likelihood of the observation of the sequences from both of the labeled and unlabeled data. We have conducted extensive experiments in two different data sets to evaluate the performance. The experimental results show that our model learned from both labeled and unlabeled data has a better performance over the model learned by only considering labeled training examples.