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Information Retrieval with a Simplified Conceptual Graph-Like Representation

机译:具有简化概念图表示的信息检索

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We argue for that taking into account semantic relations between words in the text can improve information retrieval performance. We implemented the process of information retrieval with simplified Conceptual Graph-like structures and compare the results with those of the vector space model. Our semantic representation, combined with a small simplification of the vector space model, gives better results. In order to build Conceptual Graph-like representation, we have developed a grammar based on the dependency formalism and the standard defined for Conceptual Graphs (CG). We used noun pre-modifiers and noun post-modifiers, as well as verb frames, extracted from VerbNet, as a source of definition of semantic roles. VerbNet was chosen since its definitions of semantic roles have much in common with the CG standard. We experimented on a subset of the ImageClef 2008 collection of titles and annotations of medical images.
机译:我们认为,考虑文本中单词之间的语义关系可以提高信息检索性能。我们使用简化的类似概念图的结构实现了信息检索的过程,并将结果与​​向量空间模型的结果进行了比较。我们的语义表示与矢量空间模型的少量简化相结合,可以提供更好的结果。为了构建类似概念图的表示形式,我们已经开发了一种基于依赖形式主义和为概念图(CG)定义的标准的语法。我们使用从VerbNet提取的名词前置修饰语和名词后置修饰语以及动词框架作为语义角色定义的来源。选择VerbNet是因为其语义角色的定义与CG标准有很多共同点。我们对ImageClef 2008医学图像标题和注解的子集进行了实验。

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