Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

Abstract

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.

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

“…An automated translation process that translates the text/speech from a source language to the target language forms the basis of machine translation (MT) systems. Studies like [19], used the stemming algorithm as a pre-processing step in English to the Indian language translation system. It proved the suffix separation can improve the performance of MT systems.…”

“…In these approaches, the root is extracted using lexical and syntactic rules, and most of the stemmers are based on these methods [38]. Patel and Patel [19] built a linguistic knowledge-based stemmer for the Gujarati language. Alnaied et al [39] design a linguistic rule-based stemmer for the Arabic IR system.…”

“…The second step is concerned with the positional filtration that is calculated by the Jaccard Equation (Eq. 19). The third step is to reverse the pair {𝑀 𝑗, 𝑀 𝑖 } to retrieve the value of 𝑀 𝑗 , Mateen et al [108] developed a hybrid stemmer for the Punjabi language, incorporating rule-based and table-lookup approaches.…”