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Discovering semantic relations using singular value decomposition based techniques.

机译:使用基于奇异值分解的技术发现语义关系。

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摘要

Understanding the world we live in requires access to a large amount of background knowledge: the common sense knowledge that most people know and most computer systems don't. The inability to acquire and understand semantic information, especially common sense knowledge, has constrained current artificial intelligence systems.;Much progress has been made in manually acquiring this knowledge, both in the form of lexical resources created by trained lexicographers and in those resources created by using information obtained from volunteers on the Internet. Reducing the dimensionality of this knowledge using singular value decomposition (SVD) yields a matrix representation called AnalogySpace, which reveals large-scale patterns in the data, smooths over noise, and predicts new knowledge.;Extending this work, I have created a method that uses singular value decomposition to aid in the integration of systems or representations. This technique, called blending, can be harnessed to find and exploit correlations between different resources, enabling common sense reasoning over a broader domain.;The power in blending is its ability to combine knowledge sources and to inject broad semantic and common sense knowledge into other data sets and applications. I evaluate the performance of blending in several scenarios and discuss potential applications of blending to many communities.
机译:要了解我们所生活的世界,需要获得大量的背景知识:大多数人知道而大多数计算机系统却不知道的常识知识。无法获取和理解语义信息,特别是常识知识,已经限制了当前的人工智能系统。无论是以受过训练的词典编纂者创建的词汇资源形式还是由受训的词典编纂者创建的词汇资源形式,在手动获取该知识方面都取得了很大进展使用从互联网上的志愿者那里获得的信息。使用奇异值分解(SVD)来降低知识的维数,将产生一个称为AnalogySpace的矩阵表示形式,该矩阵表示形式可以揭示数据中的大规模模式,平滑噪声并预测新知识。在扩展此工作之后,我创建了一种方法使用奇异值分解来帮助系统或表示形式的集成。这种技术称为混合,可以利用它来发现和利用不同资源之间的相关性,从而在更广泛的领域中实现常识推理。;融合的强大之处在于其能够合并知识源并将广泛的语义和常识知识注入其他知识中的能力数据集和应用程序。我评估混合在几种情况下的性能,并讨论混合在许多社区中的潜在应用。

著录项

  • 作者

    Havasi, Catherine.;

  • 作者单位

    Brandeis University.;

  • 授予单位 Brandeis University.;
  • 学科 Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 192 p.
  • 总页数 192
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
  • 关键词

  • 入库时间 2022-08-17 11:37:41

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