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计算机工程 ›› 2011, Vol. 37 ›› Issue (17): 15-18. doi: 10.3969/j.issn.1000-3428.2011.17.004

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基于MDL和LSC的语义优选方法

李东明1,张丽娟2,赵 伟1,石 晶2   

  1. (1. 吉林农业大学信息技术学院,长春 130118;2. 长春工业大学计算机科学与工程学院,长春 130012)
  • 收稿日期:2011-04-07 出版日期:2011-09-05 发布日期:2011-09-05
  • 作者简介:李东明(1979-),男,讲师、硕士,主研方向:智能信息处理,信息论;张丽娟,讲师、硕士;赵 伟,教授、博士;石 晶,讲师、博士
  • 基金资助:

    吉林省科研发展计划科技支撑基金资助重点项目(2010 0214);吉林省科技发展计划青年基金资助项目(20100155)

Semantics Preference Method Based on MDL and LSC

LI Dong-ming  1, ZHANG Li-juan  2, ZHAO Wei  1, SHI Jing  2   

  1. (1. College of Information Technology, Jilin Agricultural University, Changchun 130118, China; 2. College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China)
  • Received:2011-04-07 Online:2011-09-05 Published:2011-09-05

摘要:

为实现谓语动词对论元的自动选择,提出基于最小描述长度(MDL)和潜在语义聚类(LSC)的语义优选方法。基于MDL原则计算与动词搭配的名词的 值,根据LSC模型的EM算法求取动、名词的搭配概率P(v,n),并针对每一对动、名词计算 和P(v,n)之和,将其作为衡量两者语义关联度的标准。实验结果表明,该方法的F1值达到85.26%,优于单独使用MDL或LSC方法。

关键词: 语义优选, 最小描述长度, 潜在语义聚类, 无指导学习, 期望极大化

Abstract:

To solve automatic predicate-verb choosing for argument, this paper gives semantics preference method based on Minumum Description Length(MDL) and Latent Semantic Clustering(LSC). MDL is used to calculate of each verb-noun pair. The probabilities of a verb preferring for a noun P(v,n) is computed based on LSC model and EM is used to evaluate the parameters. For the same verb-noun pair, the sum of and P(v,n) is considered to represent the association between the verb and the noun. Experiments show the F1 reaches 85.26%, and it is better than MDL or SCL methods.

Key words: semantics preference, Minumum Description Length(MDL), Latent Semantic Clustering(LSC), unsupervised learning, Expectation Maximization(EM)

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