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基于无监督学习的部分-整体关系获取

贾真 何大可 尹红风 李天瑞

贾真, 何大可, 尹红风, 李天瑞. 基于无监督学习的部分-整体关系获取[J]. 西南交通大学学报, 2014, 27(4): 590-596. doi: 10.3969/j.issn.0258-2724.2014.04.005
引用本文: 贾真, 何大可, 尹红风, 李天瑞. 基于无监督学习的部分-整体关系获取[J]. 西南交通大学学报, 2014, 27(4): 590-596. doi: 10.3969/j.issn.0258-2724.2014.04.005
JIA Zhen, HE Dake, YIN Hongfeng, LI Tianrui. Acquisition of Part-Whole Relations Based on Unsupervised Learning[J]. Journal of Southwest Jiaotong University, 2014, 27(4): 590-596. doi: 10.3969/j.issn.0258-2724.2014.04.005
Citation: JIA Zhen, HE Dake, YIN Hongfeng, LI Tianrui. Acquisition of Part-Whole Relations Based on Unsupervised Learning[J]. Journal of Southwest Jiaotong University, 2014, 27(4): 590-596. doi: 10.3969/j.issn.0258-2724.2014.04.005

基于无监督学习的部分-整体关系获取

doi: 10.3969/j.issn.0258-2724.2014.04.005
基金项目: 

国家自然科学基金资助项目(61170111,61202043,61262058)

中央高校基本科研业务费专项基金资助项目(SWJTU11ZT08)

Acquisition of Part-Whole Relations Based on Unsupervised Learning

  • 摘要: 针对面向中文自由文本的部分-整体关系抽取问题,提出一种基于无监督学习的方法. 首先提出子模式提取算法,从领域文本集中获取概念对和概念对所在上下文模式,利用概念对和概念对上下文模式建立分布式语义模型;然后采用协同聚类算法将具有相同语义关系的概念对聚合成簇,通过训练L1正则化逻辑回归模型提取簇的特征并得到代表每个簇语义关系的概念对上下文模式;最后根据模式识别表达部分-整体关系的簇,从而获取部分-整体关系概念对. 实验结果表明,该方法取得较好的性能,F度量达到68.97%,优于传统聚类方法(55.77%)和模式匹配方法(61.95%).

     

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出版历程
  • 收稿日期:  2013-08-20
  • 刊出日期:  2014-08-25

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