Enhanced Arabic Information Retrieval System based on Arabic Text Classification

2007 ◽  
Author(s):  
Sameh Ghwanmeh ◽  
Ahmad Ababneh ◽  
Ghassan Kanaan ◽  
Riyad Al-Shalabi
Keyword(s):  
Information Retrieval ◽  
Text Classification ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Arabic Text ◽  
Arabic Information Retrieval ◽  
Arabic Text Classification

An Enhanced Text-Classification-Based Arabic Information Retrieval System

2009 ◽  
pp. 37-44
Author(s):  
Sameh Ghwanmeh ◽  
Ghassan Kannan ◽  
Riyad Al-Shalabi ◽  
Ahmad Ababneh
Keyword(s):  
Information Retrieval ◽  
Text Classification ◽  
Information Access ◽  
Main Idea ◽  
Information Retrieval System ◽  
Arabic Text ◽  
Computer Conference ◽  
Arabic Information Retrieval ◽  
A Chain ◽  
Arabic Text Classification

This chapter presents enhanced, effective and simple approach to text classification. The approach uses an algorithm to automatically classifying documents. The main idea of the algorithm is to select feature words from each document; those words cover all the ideas in the document. The results of this algorithm are list of the main subjects founded in the document. Also, in this chapter the effects of the Arabic text classification on Information Retrieval have been investigated. The goal was to improve the convenience and effectiveness of information access. The system evaluation was conducted in two cases based on precision/recall criteria: evaluate the system without using Arabic text classification and evaluate the system with Arabic text classification. A chain of experiments were carried out to test the algorithm using 242 Arabic abstracts From the Saudi Arabian National Computer Conference. Additionally, automatic phrase indexing was implemented. Experiments revealed that the system with text classification gives better performance than the system without text classification.

scholarly journals Bi-lingual Information Retrieval System for English and Italian Tweets using Python

2019 ◽  
Vol 8 (6) ◽  
pp. 1132-1135
Keyword(s):  
Information Retrieval ◽  
Text Classification ◽  
Text Analysis ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Retrieval Model ◽  
Dynamic Information ◽  
Heat Map ◽  
Use Of Social Media ◽  
Day By Day

Bi-lingual text analysis is competent in present scenario as the information gathered in various languages is flattering. The bi-lingual text classification is yet an obscure area whereas the text classification in a single language is well known. The concept of bi-lingual text has been left in a shell, apart from the lame stream of both theory as well as practical. The use of social media is increasing day by day and thus the amount of data too in increasing with a rapid rate. So, it is an alarming stage to analyze the big data and extract the useful information. In this paper, we are developing a dynamic information retrieval model and extricating the sentiments of people on global warming of English and Italian tweets and corresponding to it its heat map and affinity map are generated as it produces the output after harmonizing different objects which diverge in the rung of relevancy to the question

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