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Word sense disambiguation using implicit information

机译:使用隐式信息词感歧义

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摘要

Humans proficiently interpret the true sense of an ambiguous word by establishing association among words in a sentence. The complete sense of text is also based on implicit information, which is not explicitly mentioned. The absence of this implicit information is a significant problem for a computer program that attempts to determine the correct sense of ambiguous words. In this paper, we propose a novel method to uncover the implicit information that links the words of a sentence. We reveal this implicit information using a graph, which is then used to disambiguate the ambiguous word. The experiments show that the proposed algorithm interprets the correct sense for both homonyms and polysemous words. Our proposed algorithm has performed better than the approaches presented in the SemEval-2013 task for word sense disambiguation and has shown an accuracy of 79.6 percent, which is 2.5 percent better than the best unsupervised approach in SemEval-2007.
机译:人类通过在句子中的单词之间建立关联熟练地解释了一个模糊的单词的真正意义。完整的文本意义也是基于隐含信息,这是未明确提及的。缺乏这种隐式信息对于试图确定正确的模糊性词语感的计算机程序是一个重要问题。在本文中,我们提出了一种新的方法来揭示链接句子的单词的隐式信息。我们使用图形揭示这种隐式信息,然后将其用于消除模糊的单词。实验表明,该算法对同音异义词和多园词来说解释了正确的意义。我们所提出的算法比Semeval-2013任务的方法更好地表现出用于字发消歧的方法,并且已经表现出79.6%的准确性,比Semeval-2007中最好的无监督方法更好的2.5%。

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