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Case Based Interpretation Model for Word Sense Disambiguation in Gurmukhi

机译:基于案例的古尔穆克语词义歧义解释模型

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Language is a medium through which we can communicate our thoughts with each other. To duplicate the same communication between a human and a machine, we require Natural Language Processing. Under Artificial Intelligence, natural language processing is one of the major case study. What makes this case study more complex is the fact that a word has multiple meanings and the way in which the word is being used (context) explains the meaning of the word. This phenomenon where a single word could have multiple meaning and the correct meaning is deduced from the context it is being used as is known as Word Sense Disambiguation. In this paper, we are using Case Based Reasoning interpretation model for word sense disambiguation on Indian Regional Language - Gurmukhi, popularly known as Punjabi. The basic idea behind case based reasoning approach is to apply the solution of previously solved problems in finding the solutions for new problems. The inspiration for the case based reasoning approach came from the role of reminding in human reasoning.
机译:语言是一种我们可以相互交流思想的媒介。要在人机之间复制相同的通信,我们需要自然语言处理。在人工智能下,自然语言处理是主要的案例研究之一。这个案例研究变得更加复杂的是,一个单词具有多种含义,并且该单词的使用方式(上下文)解释了该单词的含义。从单个单词的使用上下文中可以推断出单个单词可能具有多种含义和正确含义的现象,即所谓的词义歧义消除。在本文中,我们将基于案例的推理解释模型用于消除印度地区语言-古尔穆克(Gurmukhi)(俗称旁遮普语)上的词义歧义。基于案例的推理方法背后的基本思想是将先前解决的问题的解决方案应用于新问题的解决方案中。基于案例的推理方法的灵感来自提醒在人类推理中的作用。

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