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Scalable graph-based learning applied to human language technology.

机译:可扩展的基于图的学​​习应用于人类语言技术。

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摘要

Graph-based semi-supervised learning techniques have recently attracted increasing attention as a means to utilize unlabeled data in machine learning by placing data points in a similarity graph. However, applying graph-based semi-supervised learning to natural language processing tasks presents unique challenges. First, natural language features are often discrete and do not readily reveal an underlying manifold structure, which complicates the already empirical graph construction process. Second, natural language processing problems often use structured inputs and outputs that do not naturally fit the graph-based framework Finally, scalability issues limit applicability to large data sets, which are common even in modestly-sized natural language processing applications. This research investigates novel approaches to using graph-based semi-supervised learning techniques for natural language processing, and addresses issues of distance measure learning, scalability, and structured inputs and outputs.
机译:最近,基于图的半监督学习技术作为一种通过将数据点放置在相似图中来在机器学习中利用未标记数据的手段,引起了越来越多的关注。然而,将基于图的半监督学习应用于自然语言处理任务提出了独特的挑战。首先,自然语言特征通常是离散的,不能轻易揭示底层的流形结构,这使已经经验丰富的图构造过程变得复杂。第二,自然语言处理问题通常使用不自然地适合基于图的框架的结构化输入和输出。最后,可伸缩性问题将适用性限制在大型数据集上,即使在中等大小的自然语言处理应用程序中也很常见。这项研究调查了使用基于图的半监督学习技术进行自然语言处理的新颖方法,并解决了距离度量学习,可伸缩性以及结构化输入和输出的问题。

著录项

  • 作者

    Alexandrescu, Andrei.;

  • 作者单位

    University of Washington.;

  • 授予单位 University of Washington.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 206 p.
  • 总页数 206
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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