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Performance Of The Google Desktop, Arabic Google Desktop and Peer to Peer Application in Arabic Language

Authors: Abd El Salam AL HAJJAR, Anis ISMAIL, Mohammad HAJJAR, Mazen ELSAYED;

Performance Of The Google Desktop, Arabic Google Desktop and Peer to Peer Application in Arabic Language

Abstract

ABSTRACT The Arabic language is a complex language; it is different from Western languages especially at the morphological and spelling variations. Indeed, the performance of information retrieval systems in the Arabic language is still a problem. For this reason, we are interested in studying the performance of the most famous search engine, which is a Google Desktop, while searching in Arabic language documents. Then, we propose an update to the Google Desktop to take into consideration in search the Arabic words that have the same root. After that, we evaluate the performance of the Google Desktop in this context. Also, we are interested in evaluation the performance of peer-to-peer application in two ways. The first one uses a simple indexation that indexes Arabic documents without taking in consideration the root of words. The second way takes in consideration the roots in the indexation of Arabic documents. This evaluation is done by using a corpus of ten thousand documents and one hundred different queries. KEY WORDS Search Engine, Google Desktop, Peer to Peer Application, Information retrieval, Arabic information extraction, Arabic language, Corpus.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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