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首页> 外文会议>IEEE International Conference on Cognitive Infocommunications >Object Classification as Key for Algorithmic Language Processing of Metonymy and Metaphor : A Sensory Language Retrieval Approach+
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Object Classification as Key for Algorithmic Language Processing of Metonymy and Metaphor : A Sensory Language Retrieval Approach+

机译：对象分类是转喻和隐喻算法语言处理的关键：一种感觉语言检索方法+

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This paper proposes a Sensory Language Retrieval (SLR) algorithm and implicitly initiates a new approach. The algorithm utilizes the relationship between meaning and sensory functions by assigning human sensory states to elements of spoken language. The algorithm is based on the approach accepted in cognitive linguistics stating that there is a causal relationship between body and speech. The novelty of the algorithm is that the total sensory embeddedness of cognitive functions can be evaluated by the algorithm and this is also true for the cognitive functions considered to be impersonal. In contrast to semantic and statistical approaches, the algorithm analyzes objects and functions in language based on universal sensory functions and their associated data, such as universal human functions and sensory data. The proposed algorithm assigns a new codomain to language signals whose data is capable of defining meaning and sensory patterns that have so far been difficult to identify, such as metaphorical and metonymic functions of the language.
机译：本文提出了一种感觉语言检索（SLR）算法，并隐式地提出了一种新方法。该算法通过将人类的感觉状态分配给口语元素来利用意义和感觉功能之间的关系。该算法基于认知语言学所接受的方法，该方法指出身体与语音之间存在因果关系。该算法的新颖之处在于可以通过该算法评估认知功能的总体感觉嵌入性，这对于被认为是非人性化的认知功能也是如此。与语义和统计方法相反，该算法基于通用的感觉功能及其关联数据（例如通用的人类功能和感觉数据）以语言分析对象和功能。所提出的算法为语言信号分配了一个新的共域，其数据能够定义迄今为止难以识别的含义和感觉模式，例如语言的隐喻和转喻功能。

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