emnlp emnlp2012 emnlp2012-92 emnlp2012-92-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Mahesh Joshi ; Mark Dredze ; William W. Cohen ; Carolyn Rose
Abstract: We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. First, many multidomain learning algorithms resemble ensemble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class label setting, although in practice many multidomain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multidomain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art.
Andrew Arnold, Ramesh Nallapati, and William W. Cohen. 2008. Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition. In Proceedings of ACL-08: HLT, pages 245–253. Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2007. Analysis of representations for domain adaptation. In Proceedings of NIPS 2006. Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2009. A theory of learning from different domains. Machine Learning. John Blitzer, Ryan McDonald, and Fernando Pereira. 2006. Domain Adaptation with Structural Correspondence Learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 120–128. John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 440–447. John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman. 2008. Learning Bounds for Domain Adaptation. In Advances in Neural Information Processing Systems (NIPS 2007). Giovanni Cavallanti, Nicol `o Cesa-Bianchi, and Claudio Gentile. 2008. Linear Algorithms for Online Multitask Classification. In Proceedings of COLT. Ciprian Chelba and Alex Acero. 2004. Adaptation of Maximum Entropy Capitalizer: Little Data Can Help a Lot. In Dekang Lin and Dekai Wu, editors, Proceedings of EMNLP 2004, pages 285–292. Koby Crammer, Mark Dredze, and Fernando Pereira. 2008. Exact convex confidence-weighted learning. In Advances in Neural Information Processing Systems (NIPS). Hal Daum e´ III and Daniel Marcu. 2006. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26(1): 101–126. Hal Daum e´ III, Abhishek Kumar, and Avishek Saha. 2010a. A Co-regularization Based Semi-supervised Domain Adaptation. In Neural Information Processing Systems. Hal Daum e´ III, Abhishek Kumar, and Avishek Saha. 2010b. Frustratingly Easy Semi-Supervised Domain Adaptation. In Proceedings of the ACL 2010 Workshop on Domain Adaptation for Natural Language Processing, pages 53–59. Hal Daum e´ III. 2007. Frustratingly Easy Domain Adaptation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 256–263. 1312 Hal Daum e´ III. 2009. Bayesian multitask learning with latent hierarchies. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. Thomas G. Dietterich. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40: 139–157. Mark Dredze and Koby Crammer. 2008. Online methods for multi-domain learning and adaptation. Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP ’08. Mark Dredze, Koby Crammer, and Fernando Pereira. 2008. Confidence-weighted linear classification. Proceedings of the 25th international conference on Machine learning - ICML ’08. Mark Dredze, Alex Kulesza, and Koby Crammer. 2009. Multi-domain learning by confidence-weighted parameter combination. Machine Learning, 79(1-2). Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-rui Wang, and Chih-Jen Lin. 2008. LIBLINEAR : A Library for Large Linear Classification. Journal of Machine Learning Research, 9: 1871–1874. Jenny R. Finkel and Christopher D. Manning. 2009. Hierarchical Bayesian Domain Adaptation. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 602–610. Richard Maclin and David Opitz. 1999. Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research, 11:169–198. Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. 2009. Domain Adaptation with Multiple Sources. In Proceedings of NIPS 2008, pages 1041– 1048. Kenji Sagae and Jun’ichi Tsujii. 2007. Dependency parsing and domain adaptation with lr models and parser ensembles. In Conference on Natural Language Learning (Shared Task). Avishek Saha, Piyush Rai, Hal Daum e´ III, and Suresh Venkatasubramanian. 2011. Online learning of multiple tasks and their relationships. In Proceedings of AISTATS 2011. Matt Thomas, Bo Pang, and Lillian Lee. 2006. Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In Proceedings of EMNLP, pages 327–335. Yu Zhang and Dit-Yan Yeung. 2010. A Convex Formulation for Learning Task Relationships in Multi-Task Learning. In Proceedings of the Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10).