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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network
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Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

机译：基于基于聚类分析的神经网络的自然语言识别方法研究

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摘要

Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.
机译：通过技术出现，最近在各个领域和自然语言识别中使用聚类现象。本文有助于自然语言的聚类现象，并满足知识系统的动态更新的要求。本文提出了一种基于基于神经网络的框架的句子聚类识别的动态知识提取方法。考虑句子矢量化相关问题，研究了从自然语言论文到面向对象知识系统的转换过程。本文使用各种基本定义，判断定理和后处理元素研究句子矢量化的属性。网络的句子聚类识别方法使用灵长性的概念作为句子识别结果可信度的衡量标准。使用MATLAB编写ART2神经网络仿真程序，并且对相应的分析使用神经网络对句子识别的效果。为可信度建设的可信度进行了一个选择性评估索引，并具体介绍了联合规则句子模式的实施步骤。利用新的结构建模方法来产生结构化推导关系，从而完成面向对象知识系统的自然语言知识提取过程。在这项工作中使用具有机械CAD的应用示例，以证明该示例的具体实施方式，其证实了所提出的方法的有效性。

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