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基于文本分块的多模板隐马尔可夫模型的文本信息抽取 被引量：4

Using text blocks based on multiple templates hidden markov model for text information extraction
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摘要 针对文本信息抽取中训练数据来源的多样化,不利于学习到最优的模型参数的问题,提出了一种基于多模板隐马尔可夫模型的文本信息抽取算法.新算法利用文本排版格式、分隔符等信息,对文本进行分块,在此基础上,通过对训练数据分成多个形式模板训练隐马尔可夫初始概率及转移概率参数,最后,结合统一训练的释放概率参数,对文本信息进行抽取.实验结果表明,新算法在精确度和召回率指标上比简单隐马尔可夫模型具有更好的性能. Since varied training data sources are not profitable for the learning of optimal model parameters, then a novel text information extraction algorithm based on hidden Markov model with multiple templates is proposed, which makes use of the information of format and list separators to segment text, and then extracts text information through combining the parameters of releasing probability for universal training, using multiple form templates to train the parameters of initial probability and transition probability for hidden Markov mode. Experimental results show better performance in precision and recall over simple hidden Markov model.
作者 王雷 陈治平 李志成
机构地区 福建工程学院计算机与信息科学系 创智信息技术有限公司
出处 《山东大学学报（理学版）》 CAS CSCD 北大核心 2006年第3期25-28,共4页 Journal of Shandong University(Natural Science)
基金 福建省青年科技人才创新资助项目(2005J051) 福建省自然科学基金资助项目(A0510024) 广东省关键领域重点突破资助项目(2005A10207003)
关键词 文本信息抽取 隐马尔可夫模型 多模板 文本分块 text information extraction hidden markov model multiple ten,plates text block
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献11

  • 1马亮,陈群秀,蔡莲红.一种改进的自适应文本信息过滤模型[J].计算机研究与发展,2005,42(1):79-84. 被引量：18
  • 2Yi Liu, Rong Jin, Joyce Y. A maximum coherence model for dictionary-based cross-language information retrieval[A]. Proceedings of the 28^th Annual International ACM SIGIR Conference[C]. Salvador, Brazil: ACM Press, 2005. 536-543.
  • 3Kushmerick N. Wrapper induction: Efficiency and expressiveness[J]. Artificial Intelligence Journal, 2000, 118(12): 15-68.
  • 4刘云中,林亚平,陈治平.基于隐马尔可夫模型的文本信息抽取[J].系统仿真学报,2004,16(3):507-510. 被引量：51
  • 5Dayne Frietag, Andrew McCallum. Information extraction with HMMs and shrinkage[A]. Proceedings of the, AAAI'99 Workshop on Machine Learning for Information Extraction[C]. Orlando, US: AAAI Press/The MIT Press, 1999. 31 - 36.
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  • 7Souyma Ray, Mark Graven. Representing sentence structure in hidden markov models for information extraction[A]. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence[C].Washington, US: Morgan Kaufmann Publishers, 2001. 1273 - 1279.
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二级参考文献40

  • 1[1]A. McCallum, K. Nigam, J. Rennie, and K. Seymore. A machine learning approach to building Domain-Specific Search Engines [A]. In Proceedings of IJCAI-99 [C]. 622-667.
  • 2[2]Ellien Riloff. Automatically Constructing a Dictionary for Information Extraction Task [A]. Proceeding for the Eleventh National Conference on Artificial Intelligence [C]. 1993. 811-816.
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  • 5[5]Kushmerick, N. Wrapper induction: efficiency and Expressiveness [J]. Artificial Intelligence,2000, Vol. 118, pp. 15--68.
  • 6[6]Leek,T. R. Information Extraction Using Hidden Markov Models [D]. Master's thesis, UC san Diego,1997.
  • 7[7]Kristie Seymore, Andrew McCallum, Ronal Rosenfel. Learning Hidden Markov Model Structure for Information Extract [A]. AAAI' 99 Workshop on Machine Learning for Information Extraction [C]. 1999. 37-42.
  • 8[8]Dayne Frietag, Andrew McCallum. Information Extraction with HMMs and shrinkage [A]. In Proceedings of the AAAI'99 Workshop on Machine Learning for Information Extraction [C], 1999, pp. 31-36.
  • 9[9]Freitag, D., & McCallum, A. Information extraction with HMM structures learned by stochastic optimization [A]. Proceedings of the Eighteenth Conference on Artificial Intelligence [C]. 2000.584-589.
  • 10[10]Freitag, D., McCallum, A., and Pereira F. Maximum Entropy Markov Models for Information Extraction and Segmentation [A]. In proceedings of ICML-2000 [C]. 591-598.

共引文献111

同被引文献26

引证文献4

二级引证文献27

山东大学学报（理学版）
2006年 第3期

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