|
[1]R. K. Ando and T. Zhang, “A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data,” J Mach Learn Res, vol. 6, pp. 1817–1853, Dec. 2005. [2]A. Aue and M. Gamon, “Customizing sentiment classifiers to new domains: A case study,” In Proceedings of recent advances in natural language processing (RANLP), 2005, vol. 1, pp. 2–1. [3]J. Gao, W. Fan, J. Jiang, and J. Han, “Knowledge transfer via multiple model local structure mapping,” In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2008, pp. 283–291. [4]S. Li and C. Zong, “Multi-domain adaptation for sentiment classification: Using multiple classifier combining methods,” In Natural Language Processing and Knowledge Engineering, 2008. NLP-KE’08. International Conference on, 2008, pp. 1–8. [5]R. Xia and C. Zong, “A POS-based ensemble model for cross-domain sentiment classification,” In Proceedings of the 5th international Joint conference on natural Language Processing (iJcnLP-2010), 2011. [6]Q. Zhou, Y. Zhang, and X. Hu, “An Ensemble Method Based on Confidence Probability for Multi-domain Sentiment Classification,” In Intelligent Computing Technology, Springer, 2012, pp. 214–220. [7]A. B. Goldberg and X. Zhu, “Seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization,” In Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, 2006, pp. 45–52. [8]J. Meng and H. Lin, “Transfer learning based on graph ranking,” In Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on, 2012, pp. 1353–1357. [9]N. Ponomareva and M. Thelwall, “Do neighbours help?: an exploration of graph-based algorithms for cross-domain sentiment classification,” In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 655–665. [10]Q. Wu, S. Tan, and X. Cheng, “Graph ranking for sentiment transfer,” In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, Stroudsburg, PA, USA, 2009, pp. 317–320. [11]Q. Wu and S. Tan, “A two-stage framework for cross-domain sentiment classification,” Expert Syst. Appl., May 2011. [12]R. K. Ando and T. Zhang, “A high-performance semi-supervised learning method for text chunking,” In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005, pp. 1–9. [13]A. Andreevskaia and S. Bergler, “When specialists and generalists work together: Overcoming domain dependence in sentiment tagging,” Proc. Acl-08 Hlt, pp. 290–298, 2008. [14]J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification,” In Annual Meeting-Association For Computational Linguistics, 2007, vol. 45, p. 440. [15]J. Blitzer, R. McDonald, and F. Pereira, “Domain adaptation with structural correspondence learning,” In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA, USA, 2006, pp. 120–128. [16]W. Dai, G.-R. Xue, Q. Yang, and Y. Yu, “Co-clustering based classification for out-of-domain documents,” In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, pp. 210–219. [17]H. Daumé and D. Marcu, “Frustratingly easy domain adaptation,” In Annual meeting-association for computational linguistics, 2007, vol. 45, p. 256. [18]S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain sentiment classification via spectral feature alignment,” In Proceedings of the 19th international conference on World wide web, 2010, pp. 751–760. [19]D. Bollegala, D. Weir, and J. Carroll, “Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification,” In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011, vol. 1, pp. 132–141. [20]N. Ponomareva and M. Thelwall, “Biographies or blenders: Which resource is best for cross-domain sentiment analysis?,” In Computational Linguistics and Intelligent Text Processing, Springer, 2012, pp. 488–499. [21]C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” Acm Trans Intell Syst Technol, vol. 2, no. 3, pp. 27:1–27:27, May 2011. [22]S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” Ieee Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, Oct. 2010. [23]C. Whitelaw, N. Garg, and S. Argamon, “Using appraisal groups for sentiment analysis,” In Proceedings of the 14th ACM international conference on Information and knowledge management, 2005, pp. 625–631. [24]B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Found Trends Inf Retr, vol. 2, no. 1–2, pp. 1–135, Jan. 2008. [25]B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” In Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10, Stroudsburg, PA, USA, 2002, pp. 79–86. [26]B. Plank and G. van Noord, “Effective measures of domain similarity for parsing,” In Proceedings of ACL, 2011, pp. 1566–1576. [27]V. Van Asch and W. Daelemans, “Using domain similarity for performance estimation,” In Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing, 2010, pp. 31–36.
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