This paper presents a method to measure the semantic relatedness between sentences by using conceptual graph. To compare the sentence meaning, we firstly convert an English sentence into a conceptual graph. Then we employ the conceptual graph operations to match two conceptual graphs of one sentence and another. In matching nodes in the conceptual graphs, we utilize two lexicons i.e. WordNet and VerbNet. All semantic relatedness of contents in concept nodes are computed by using WordNet. The VerbNet is another used to find the semantic relatedness of contents in conceptual relation nodes. By our specified rules, the semantic relatedness between two sentences are objectively scored. We evaluate the performance of the proposed measurement with "Microsoft Research Paraphrase Corpus". The experimental results show the% correctness as 80.00% compared to human judgment. Moreover, we apply the measurement with words sense ambiguity analysis, the proposed measurement yields 76.44% of correctness compared to human judgment.
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机译：本文介绍了一种通过使用概念图来测量句子之间的语义相关性的方法。要比较句子意义，我们首先将英语句子转换为概念图。然后我们采用概念图操作来匹配一个句子的两个概念图。在概念图中的匹配节点中，我们利用两个词汇网和手册。通过使用Wordnet计算概念节点中的内容的所有语义相关性。动词网是另一个用于在概念关系节点中找到内容的语义相关性。通过我们指定的规则，客观地评分两种句子之间的语义相关性。我们评估拟议测量的绩效“Microsoft Research XAphrase语料库”。与人类判断相比，实验结果显示为80.00％的百分比。此外，我们使用单词感测模糊性分析来应用测量，与人类判断相比，所提出的测量结果产生76.44％的正确性。
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