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Using Text Comprehension Model for Learning Concepts, Context, and Topic of Web Content

机译:使用文本理解模型学习Web内容的概念,上下文和主题

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Concepts in web ontologies help machines to understand data through the meanings they hold. Furthermore, learning contexts and topics of web documents also have helped in better semantic-oriented structuring and retrieval of data on the web. In this short paper we present a novel approach for domain-independent open learning of domain concepts, context and topic of any given Web document. Our approach is based on a computational version of the Construction-Integration (CI) model of text comprehension. Our proposed system mimics the way humans learn the meanings of textual units and identify domain concepts, contexts and topics in the form of semantic networks. We apply our system on a number of web documents with a range of topics and domains. The resulting semantic networks provide a quantitative and qualitative insights into the nature of the given web documents.
机译:Web本体中的概念可帮助机器通过它们所具有的含义来理解数据。此外,学习Web文档的上下文和主题还有助于更好地面向语义地构造和检索Web上的数据。在这篇简短的论文中,我们提出了一种新颖的方法,用于对任何给定Web文档的领域概念,上下文和主题进行与领域无关的开放式学习。我们的方法基于文本理解的构建集成(CI)模型的计算版本。我们提出的系统模仿了人类学习文本单位的含义并以语义网络的形式识别领域概念,上下文和主题的方式。我们将我们的系统应用于一系列主题和领域广泛的Web文档。由此产生的语义网络提供了对给定Web文档性质的定量和定性见解。

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