孙世昶;林鸿飞;孟佳娜;刘洪波. 利用源域结构的粒迁移学习及词性标注应用[J]. 中文信息学报, 2017, 31(1): 66-74.
SUN Shichang; LIN Hongfei; MENG Jiana; LIU Hongbo. Exploiting Source Domain Structure in Granular Transfer Learning for Part-of-speech Tagging. , 2017, 31(1): 66-74.
Exploiting Source Domain Structure in Granular Transfer Learning for Part-of-speech Tagging
SUN Shichang1,2, LIN Hongfei1, MENG Jiana2, LIU Hongbo3
1. School of Computer Science and Technology, Dalian University of Technology, Liaoning, Dalian 116023, China 2. School of Computer Science and Technology, Dalian Nationality University, Liaoning, Dalian 116600, China 3. Information Science and Technology College, Dalian Maritime University, Liaoning, Dalian 116026, China
Abstract：Transfer learning alleviates the data sparseness issue to some extent, but the generalization capacity is still hindered by negative-transfer problem. To address this issue, we propose an information granulation method for text corpora based on source domain structure. Interval granules are employed to express the influence of source domain structure on statistics of the dataset. We further design an Interval Type-2 fuzzy Hidden Markov Model (IHMM) to deal with the interval granules. Experiments on part-of-speech tagging proves that the proposed method avoids negative-transfer and improves generalization capacity.
[1] Pan S J, Yang Q. A Survey on Transfer Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359. [2] Pedrycz W. Granular computing: analysis and design of intelligent systems[M]. CRC press, 2013. [3] Pedrycz W, Russo B, Succi G. Knowledge transfer in system modeling and its realization through an optimal allocation of information granularity[J]. Applied Soft Computing, 2012, 12(8): 1985-1995. [4] Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Readings in Spoech Recognition, 1990, 77(2): 267-296. [5] Walder C J, Kootsookos B C, Peter J. andLovell. Towards a Maximum Entropy Method for Estimating HMM Parameters[C]//Proceedings of INTERSPEECH. 2003: 45-49. [6] Liu J, Yu K, Zhang Y, et al. Training Conditional Random Fields Using Transfer Learning for Gesture Recognition[C]//Proceedings of IEEE International Conference on Data Mining. 2010: 314-323. [7] Sutton C, McCallum A. Composition of conditional random fields for transfer learning[C]//Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005: 748-754. [8] Brants T. TnT: a statistical part-of-speech tagger[C]//Proceedings of the Sixth Conference on Applied Natural Language Processing. 2000: 224-231. [9] Ait-Mohand K, Paquet T, Ragot N. Combining structure and parameter adaptation of HMMs for printed text recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(9): 1716-1732. [10] Kim N S, Sung J S, Hong D H. Factored MLLR Adaptation[J]. Signal Processing Letters, 2011, 18(2): 99-102. [11] 郭虎升, 王文剑. 动态粒度支持向量回归机[J]. 软件学报, 2013, 24(11): 2535-2547. [12] 邱桃荣. 面向本体学习的粒计算方法研究[D]. 北京交通大学博士学位论文, 2009. [13] Song M, Pedrycz W. Granular neural networks: concepts and development schemes[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(4): 542-553. [14] 孟军. 相容粒计算模型及其数据挖掘研究[D]. 大连理工大学博士学位论文, 2012.
2017, Vol. 31 
孙世昶(1979—),博士,讲师,主要研究领域为机器学习与文本挖掘。E-mail: ssc@dlnu.edu.cn
林鸿飞(1962—),通信作者,博士,教授,主要研究领域为文本挖掘和信息检索。E-mail: lhf@dlut.edu.cn
孟佳娜 (1972—),博士,教授,主要研究领域为文本挖掘。E-mail: mjn@dlnu.edu.cn