Using the Structure of a Conceptual Network in Computing Semantic Relatedness
Using the Structure of a Conceptual Network in Computing Semantic Relatedness
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DOI：
10.1007/11562214_67
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发表时间：
2005-10
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影响因子：
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通讯作者：
Iryna Gurevych
中科院分区：
文献类型：
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作者：
Iryna Gurevych
提出了一种计算概念语义相关性的新方法。该方法仅依赖于概念网络的结构，不需要执行额外的语料库分析。利用网络结构生成人工概念光泽。它们取代了由人类编写的文本定义，并由基于词典的语义相关性度量[1]进行处理。我们在GermaNet （WordNet的德国版本）的基础上实现了这个度量，并在一个由57个词对组成的德语数据集上评估了结果，这些词对是由人类受试者根据其语义相关性进行评级的。我们的方法可以很容易地应用于计算基于替代概念网络的语义相关性，例如在生命科学领域。
We present a new method for computing semantic relatedness of concepts. The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis. The network structure is employed to generate artificial conceptual glosses. They replace textual definitionsproperwritten by humans and are processed by a dictionary based metric of semantic relatedness [1]. We implemented the metric on the basis of GermaNet, the German counterpart of WordNet, and evaluated the results on a German dataset of 57 word pairs rated by human subjects for their semantic relatedness. Our approach can be easily applied to compute semantic relatedness based on alternative conceptual networks, e.g. in the domain of life sciences.