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An Approach for Finding Semantic Relatedness Score Between Two Sentences Based on their Senses

机译:基于敏感的两个句子中查找语义相关性分数的方法

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Finding semantic relatedness score between two sentences is useful in many research areas. Existing relatedness methods do not consider its sense while computing semantic relatedness score between two sentences. In this study, a Word Sense Disambiguation (WSD) method is proposed which is used in finding the sense-oriented sentence semantic relatedness measure. The WSD method is used to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet lexical dictionary and the Wikipedia corpus. The sense-oriented sentence semantic relatedness measure combines edge-based score between words depending the context of the sentence; sense based score which finds sentences having similar senses; as well as word order score. We have evaluated the proposed WSD method on publicly available English WSD corpora. We have compared our proposed sense-oriented sentence semantic relatedness measure on standard datasets. Experimental analysis illustrates the significance of proposed method over many baseline and current systems like Lesk, UKB, IMS, Babelfy.
机译:在许多研究领域,在两个句子之间发现语义相关性分数很有用。现有的相关性方法在计算两个句子之间的语义相关性得分时,不考虑其意义。在这项研究中,提出了一种单词感应消除歧义(WSD)方法,用于找到面向感测的句子语义相关性测量。 WSD方法用于找到句子中存在的正确词。该方法使用Wordnet词汇字典和维基百科语料库。根据句子的上下文,指向句子语义相关性测量结合了单词之间的边缘分数;感知基本得分,用于找到具有相似感应的句子;以及单词订单得分。我们在公开可用的英语WSD Corpora上评估了拟议的WSD方法。我们对标准数据集的建议面向的句子语义相关性测量进行了比较。实验分析说明了所提出的方法在许多基线和当前系统中的意义,如Lesk,UKB,IMS,Babelfy。

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