Multilayer Neural Network Technique for Parsing the Natural Language Sentences

M. Singh, Sukrati Chaturvedi, Deepak Shudhalwar
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引用次数: 2

Abstract

In this article is presented an approach for parsing natural language sentences using neural networks. The pre-processing technique is applied to code the sentences into string of bits and after the training process is started, is formed into patterns available in the form of coded information. The multilayer feed forward networks are used here for training to classify the words into appropriate syntactical categories. The classified words represent the parsed information of the given sentences. The main function of the network is to assign the respective syntactical categories to each word of a sentence with a minimal error rate. The comparison between the two popular neural network approaches i.e. feed forward neural network and radial basis neural network is presented to analyze performance for the new and unknown sentences.
自然语言句子分析的多层神经网络技术
本文提出了一种利用神经网络分析自然语言句子的方法。采用预处理技术将句子编码为位串,训练过程开始后,形成以编码信息形式提供的模式。这里使用多层前馈网络进行训练,将单词分类到适当的句法类别中。分类词表示给定句子的解析信息。该网络的主要功能是以最小的错误率为句子中的每个单词分配各自的句法类别。比较了前馈神经网络和径向基神经网络这两种常用的神经网络方法对新句子和未知句子的处理性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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