Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

Автор: Chandrakant D. Patel, Jayesh M. Patel

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 3 vol.13, 2021 года.

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With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing

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Stemming, Gujarati Language, Supervised algorithms, Machine Learning, Accuracy

Короткий адрес: https://sciup.org/15017740

IDR: 15017740   |   DOI: 10.5815/ijisa.2021.03.03

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