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引用次数: 0

摘要

在人工智能、知识库、自然语言、语义和逻辑等领域已经做了大量的工作,通过使用树搜索方法、模式识别、神经网络等,我们现在开始有系统可以像人类一样理解一个词或一个句子的意思,但这些系统还不够灵活或成熟,无法真正应用于实际应用。这个问题主要来自于所使用的处理方法、理解单词和句子的方法以及非动态识别行为。因此,本文介绍了一种基于神经网络的语义和逻辑处理方法,它以一种独特的方式将单词和句子转化为神经网络和动态行为-完成目标。在这些过程中,我发现一个句子有一个与某一知识相关的意义,这种从句子到知识的转换有一种独特的知识压缩方法。因此,我还在语义和逻辑方面引入了一种知识压缩方法。
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Simple pre-processor for semantics and logic
Lots of work has been done in the field of AI, knowledge bases, natural languages, semantics and logic by using the tree search method, pattern recognition, neural networks, etc., and we are now beginning to have systems which can understand the meaning of a word or a sentence as a human does, but these systems are not flexible or mature enough for real use, and so not yet applicable for real use. This problem mainly comes from the processing methods used, the methods used to understand words and sentences, and the non-dynamic recognition behaviours. So, in this paper, I introduce a semantic and logic processing method, using neural networks, which has a unique way of transforming words and sentences into neural networks and dynamical behaviourism-accomplishing objectives. As a result of these processes, I found that a sentence has a meaning that is related to certain knowledge, and this sentence-to-knowledge transformation has a unique knowledge compression method. Therefore, I also introduce a knowledge compression method in semantics and logic.
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