Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Applying co-training methods to statistical parsing
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
A probability model to improve word alignment
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Alignment model adaptation for domain-specific word alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Stochastic lexicalized inversion transduction grammar for alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Word sense disambiguation with semi-supervised learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Active learning-based elicitation for semi-supervised word alignment
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Active semi-supervised learning for improving word alignment
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Improving word alignment by semi-supervised ensemble
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the supervised boosting algorithm to a semi-supervised learning algorithm by incorporating the unlabeled data. In this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. Then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data. Based on this semi-supervised boosting algorithm, we investigate two boosting methods for word alignment. In addition, we improve the word alignment results by combining the results of the two semi-supervised boosting methods. Experimental results on word alignment indicate that semi-supervised boosting achieves relative error reductions of 28.29% and 19.52% as compared with supervised boosting and unsupervised boosting, respectively.