Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Incorporating speaker and discourse features into speech summarization
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Domain adaptation of natural language processing systems
Domain adaptation of natural language processing systems
Structural correspondence learning for parse disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A skip-chain conditional random field for ranking meeting utterances by importance
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Summarizing spoken and written conversations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Interpretation and transformation for abstracting conversations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Frustratingly easy semi-supervised domain adaptation
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
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We are interested in improving the summarization of conversations by using domain adaptation. Since very few email corpora have been annotated for summarization purposes, we attempt to leverage the labeled data available in the multiparty meetings domain for the summarization of email threads. In this paper, we compare several approaches to supervised domain adaptation using out-of-domain labeled data, and also try to use unlabeled data in the target domain through semi-supervised domain adaptation. From the results of our experiments, we conclude that with some in-domain labeled data, training in-domain with no adaptation is most effective, but that when there is no labeled in-domain data, domain adaptation algorithms such as structural correspondence learning can improve summarization.