Barq: Distributed multilingual internet search engine with focus on Arabic language

Rachidi, Tajjeeddine; Iraqi, Omar; Bouzoubaa, M.; Khattab, Aya; Kourdi, M. El; Zahi, Azeddine; Bensaid, A.

doi:10.1109/icsmc.2003.1243853

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Furthermore, in [29,30], the authors used query expansion with three thesauri in their Barq system (multilingual search engine with a focus on the Arabic language). The use of the query expansion method showed a rise of approximately 75 % in recall.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Aldayel

Ykhlef

2015

Arab J Sci Eng

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Query paraphrasing aims to construct a better formulation of user queries in order to enhance retrieval. Formulating search queries remains complicated for a subset of Web users. In a typical situation, a user will not receive satisfactory results from the submitted search query and will subsequently attempt different query paraphrases. The Arabic vocabulary is rich in synonyms and hyponyms. Such richness of synonyms makes automation of the paraphrasing technique crucial for Arabic information retrieval systems in order to facilitate the process of paraphrasing synonyms. In this article, we propose an enhancement for Arabic information retrieval using a query paraphrasing technique. Furthermore, two query paraphrasing optimization techniques are proposed to overcome the time complexity and exhaustive calculation of existing query paraphrasing techniques. One of these techniques uses a genetic algorithm (GA-QP), and the other employs the artificial bee colony algorithm (ABC-QP). The performance of these two algorithms is compared. ABC-QP shows an improvement in Arabic information retrieval performance compared with the genetic algorithm query paraphrasing system. B Abeer Al-Dayel

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Section: Related Workmentioning

confidence: 99%

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Aldayel

Ykhlef

2015

Arab J Sci Eng

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“…Billhardt, Borrajo, and Maojo in [8] use term co-occurrence data for document indexing. Authors in [9] provide a multilingual Internet search engine that includes Arabic word stemming. Stemming is performed by eliminating postfix, prefix, infix and parts of words, and also by reducing plural, singular and adjective forms into a canonical form.…”

Section: Related Workmentioning

confidence: 99%

Verifying Hadith Correctness in Islamic Web Pages using Information Retrieval Techniques

Shatnawi¹,

Abuein²,

Darwish³

2012

IJCA

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Certain types of web information need to be verified for correctness once being posted on the web (i.e. the information has a reference that cannot be tampered with). Such these types of information need systems in order to verify and assure their correctness. "The Hadith"; which is the sayings of Prophet Mohammad is an example of these types of information. Hadith in Islam is a very important tool to understand Qur'an, and in matter of Islamic law. This paper presents a reliable method to extract Hadith text from web pages, in order to find out the degree of correctness for each one; by looking it up in Sheikh Al-Albani Hadith Database (correct series and weak series) which contains 17000+Hadeeth texts and their degrees of correctness according to Sheikh Al-Albani studies. The proposed approach uses positional index built over the Sheikh Al-Albani Hadith Database (SAHDB) and extracts Hadith terms as queries from the passed web page, and execute these queries over SAHDB index.

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“…Spelling correction modules are vital components for many real-world NLP applications such as Optical Character Recognition (OCR) systems where spelling correction is employed in the post-processing phase to possibly correct any misspelled word [8]. Moreover, it is used for search query correction to ensure the quality of search-based systems [9].…”

Section: Introductionmentioning

confidence: 99%

AraSpell: A Deep Learning Approach for Arabic Spelling Correction

Salhab

Abu-Khzam

2023

Preprint

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Spelling correction is the task of identifying spelling mistakes, typos, and grammatical mistakes in a given text and correcting them according to their context and grammatical structure. This work introduces "AraSpell," a framework for Arabic spelling correction using different seq2seq model architectures such as Recurrent Neural Network (RNN) and Transformer with artificial data generation for error injection, trained on more than 6.9 Million Arabic sentences. Thorough experimental studies provide empirical evidence of the effectiveness of the proposed approach, which achieved 4.8% and 1.11% word error rate (WER) and character error rate (CER), respectively, in comparison with labeled data of 29.72% WER and 5.03% CER. Our approach achieved 2.9% CER and 10.65% WER in comparison with labeled data of 10.02% CER and 50.94% WER. Both of these results are obtained on a test set of 100K sentences.

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Barq: Distributed multilingual internet search engine with focus on Arabic language

Cited by 9 publications

References 8 publications

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Verifying Hadith Correctness in Islamic Web Pages using Information Retrieval Techniques

AraSpell: A Deep Learning Approach for Arabic Spelling Correction

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