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Multilayer Neural Network Technique for Parsing the Natural Language Sentences

Multilayer Neural Network Technique for Parsing the Natural Language Sentences

Manu Pratap Singh, Sukrati Chaturvedi, Deepak D. Shudhalwar
Copyright: © 2022 |Pages: 19
ISBN13: 9781668456828|ISBN10: 1668456826|EISBN13: 9781668456835
DOI: 10.4018/978-1-6684-5682-8.ch028
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MLA

Singh, Manu Pratap, et al. "Multilayer Neural Network Technique for Parsing the Natural Language Sentences." Research Anthology on Applied Linguistics and Language Practices, edited by Information Resources Management Association, IGI Global, 2022, pp. 595-613. https://doi.org/10.4018/978-1-6684-5682-8.ch028

APA

Singh, M. P., Chaturvedi, S., & Shudhalwar, D. D. (2022). Multilayer Neural Network Technique for Parsing the Natural Language Sentences. In I. Management Association (Ed.), Research Anthology on Applied Linguistics and Language Practices (pp. 595-613). IGI Global. https://doi.org/10.4018/978-1-6684-5682-8.ch028

Chicago

Singh, Manu Pratap, Sukrati Chaturvedi, and Deepak D. Shudhalwar. "Multilayer Neural Network Technique for Parsing the Natural Language Sentences." In Research Anthology on Applied Linguistics and Language Practices, edited by Information Resources Management Association, 595-613. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-5682-8.ch028

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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.

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