On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Incorporating Prior Knowledge into Boosting
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Language Model Adaptation Using Mixtures and an Exponentially Decaying Cache
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Learning a perceptron-based named entity chunker via online recognition feedback
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Reranking and self-training for parser adaptation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Improving the scalability of semi-Markov conditional random fields for named entity recognition
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Chinese segmentation and new word detection using conditional random fields
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Conditional Random Fields (CRFs) have received a great amount of attentions in many fields and achieved good results. However, a case frequently encountered in practice is that the test data's domain is different with the training data's. It would affect negatively the performance of CRFs. This paper presents a novel technique for maximum a posteriori (MAP) adaptation of Conditional Random Fields model. The background model, which is trained on data from a domain, could be well adapted to a new domain with a small number of labeled domain specific data. Experimental results on tasks of chunking and capitalizing show that this technique can significantly improve performance on out-of-domain data. In chunking task, the relative improvement given by the adaptation technique is 56.9%. With two in-domain sentences, it also can achieve 30.2% relative improvement.