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Stemmer Impact on Quranic Mobile Information Retrieval Performance

Huda Omar, Mohammed Dahab, Mahmoud Kamal
2016 International Journal of Advanced Computer Science and Applications  
This paper aims to study the impact of using stemming techniques in mobile effectiveness. Two-word extraction stemming techniques will be used: a light stemmer and a dictionary-lookup stemmer.  ...  Stemming algorithms are employed in information retrieval (IR) to reduce verity variants of the same word with several endings to a standard stem.  ...  They also appreciate the efforts of the Zekr Quran Project and Quran Code Desktop Software, which was used to obtain the relevant judgments for the query terms.  ... 
doi:10.14569/ijacsa.2016.071218 fatcat:33fyuqc3zrbnbcilmuqd3gehn4

Answering English Queries in Automatically Transcribed Arabic Speech

Abdusalam F. A. Nwesri, S. M. M. Tahaghoghi, Falk Scholer
2007 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007)  
rely on machine translation of the underlying text.  ...  There are several well-known approaches to parsing Arabic text in preparation for indexing and retrieval.  ...  The techniques have a clear impact on retrieval performance: with Figure 4.  ... 
doi:10.1109/icis.2007.61 dblp:conf/ACISicis/NwesriTS07 fatcat:u7vfwxraereejafmvngcglgwpm

Arabic Information Retrieval: A Relevancy Assessment Survey

Ahmad Hussein Ababneh, Joan Lu, Qiang Xu
2016 Information Systems Development  
The paper presents a research in Arabic Information Retrieval (IR). It surveys the impact of statistical and morphological analysis of Arabic text in improving Arabic IR relevancy.  ...  Query expansion and Text Translation showed positive relevancy effect. However, other tasks such as NER and TS still need more research to realize their impact on Arabic IR.  ...  The important matter in this context is to measure the impact of stemming On Arabic IR. In [23] three stemmers were tested, Al-Stem 19 , UMass, and Modified UMass stemmers.  ... 
dblp:conf/isdevel/AbabnehLX16 fatcat:3xi3teoqbfaw5j2fpxnmhu6slu

TREC 2002 Cross-lingual Retrieval at BBN

Alexander M. Fraser, Jinxi Xu, Ralph M. Weischedel
2002 Text Retrieval Conference  
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied of the Defense  ...  We are currently analyzing the impacts of the individual changes on retrieval. Table 1 : Description of sumbitted runs for TREC 2002 CLIR.  ...  Our experiments featured a different method for estimating general English probabilities, two additional Arabic stemmers, a more complex model for lexicon extraction from parallel texts and a slightly  ... 
dblp:conf/trec/FraserXW02 fatcat:qusmsvco5vfqxdb5d3747g4r44

On the Impact of Dataset Characteristics on Arabic Document Classification

Diab Abuaiadah, Jihad El Sana, Walid Abusalah
2014 International Journal of Computer Applications  
This paper describes the impact of dataset characteristics on the results of Arabic document classification algorithms using TF-IDF representations.  ...  The experiments compared different stemmers, different categories and different training set sizes, and found that different dataset characteristics produced widely differing results, in one case attaining  ...  Additionally, Al-Shammari and Lin [8] show that stemmers could improve the accuracy of automatic Arabic text processing.  ... 
doi:10.5120/17701-8680 fatcat:uesm6jxxb5at7dyssk3uqhxyz4

Developing Two Different Novel Techniques for Arabic Text Stemming

Mohammad Mustafa, Afag Salah Aldeen, Mohammed E. Zidan, Rihab E. Ahmed, Yasir Eltigani
2019 Intelligent Information Management  
Several techniques have been proposed to stemming Arabic text and among them, Khoja and light-10 stemmers are the most widely used.  ...  Stemming is used to produce stem or root of words. The process is vital to different research fields such as text mining, sentiment analysis, and text categorization, etc.  ...  Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper.  ... 
doi:10.4236/iim.2019.111001 fatcat:2dcy3aw7jncv3bnecekl3pj5li

Automated arabic text classification with P-Stemmer, machine learning, and a tailored news article taxonomy

Tarek Kanan, Edward A. Fox
2015 Journal of the Association for Information Science and Technology  
The Arabic language has many grammatical forms, varieties of word synonyms, and different word meanings that vary depending on factors like word order and inclusion of diacritics.  ...  We developed tailored stemming (i.e., a new Arabic light stemmer) and automatic classification methods (the best being binary SVM classifiers) to work with the taxonomy.  ...  This research was made possible by NPRP grant # 4-029-1-007 from the Qatar National Research Fund (a member of Qatar Foundation).  ... 
doi:10.1002/asi.23609 fatcat:lzsmz2t3p5bcpnig4gdgpatkia

A Study of the Effects of Stemming Strategies on Arabic Document Classification

Yousif A. Alhaj, Jianwen Xiang, Dongdong Zhao, Mohammed A. A. Al-Qaness, Mohamed Abd Elaziz, Abdelghani Dahou
2019 IEEE Access  
This paper aims to study the impact of stemming techniques, namely Information Science Research Institute (ISRI), Tashaphyne, and ARLStem on Arabic DC.  ...  Experiments are conducted on CNN Arabic corpus, which is collected from Arabic websites to assess the performance of the classification system.  ...  [32] studied the impact of stemming techniques on Arabic sentiment analysis.  ... 
doi:10.1109/access.2019.2903331 fatcat:dzob3zns6ndenbt3tasp46722q

Text mining: A survey of Arabic root extraction algorithms

Hamza et al., Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia, Faculty of Computer Science and Information Technology, Omdurman Islamic University, Omdurman, Sudan
2021 International Journal of Advanced and Applied Sciences  
In all Arab countries, the Arabic language is the official language spoken and written and is one of the oldest known languages.  ...  The basic algorithms are used to extract and classify texts, information retrieval systems, and indexes. Algorithms are used to extract the root of a word from different natural languages.  ...  We can use stemming algorithms in text mining, text classification, information retrieval systems, and indexers. A lot of stemming algorithms are built in different natural languages.  ... 
doi:10.21833/ijaas.2021.01.002 fatcat:4girld4edfc2zc2bceukd5xpp4

Arabic Document Classification Using Multiword Features

Diab Abuaiadah
2013 International Journal of Computer and Communication Engineering  
Accordingly it presents more challenges when enhancing Arabic information retrieval to a level comparable to English.  ...  Weinvestigate the use of multiword features to improve Arabic document classification. The Arabic language is both morphologically rich and highly inflected.  ...  In this work we conduct several experiments to explore the impact of multiword features on Arabic document classification.  ... 
doi:10.7763/ijcce.2013.v2.269 fatcat:bmtidmfzerdipeddjlc5ipfznu

The effects of Pre-Processing Techniques on Arabic Text Classification

2021 International Journal of Advanced Trends in Computer Science and Engineering  
This paper investigates the impact of wisely selected preprocessing techniques on the efficiency of different text classification algorithms.  ...  In the last two decades, the amount of available Arabic text data on the World Wide Web is dramatically growing, making it the fourth most used language on the web.  ...  The performed experiments confirm that well-selected preprocessing techniques have a great impact on Arabic text classification.  ... 
doi:10.30534/ijatcse/2021/061012021 fatcat:r4iauozdtnfj3ppwcambyw5oee

Arabic Text Summarization Based on Latent Semantic Analysis to Enhance Arabic Documents Clustering

Hanane Froud, Abdelmonaime Lachkar, Said Alaoui Ouatik
2013 International Journal of Data Mining & Knowledge Management Process  
In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity  ...  Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present  ...  It may impact positively or negatively on the accuracy of any Text Mining tasks; therefore the choice of the preprocessing approaches will lead by necessity to the improvement of any Text Mining tasks  ... 
doi:10.5121/ijdkp.2013.3107 fatcat:auibx4p3ffa5jczj37enm4qdbu

A novel Arabic lemmatization algorithm

Eiman Al-Shammari, Jessica Lin
2008 Proceedings of the second workshop on Analytics for noisy unstructured text data - AND '08  
We investigate the impact of our new lemmatizer on unsupervised data mining techniques in comparison to the leading Arabic stemmers.  ...  Tokenization is a fundamental step in processing textual data preceding the tasks of information retrieval, text mining, and natural language processing.  ...  Stemming Arabic documentations was done manually prior to TREC (Text Retrieval Conference) and only applied on small corpora.  ... 
doi:10.1145/1390749.1390767 dblp:conf/sigir/Al-ShammariL08 fatcat:mywb6fggsne6vp2evtoy3f5isu

Arabic text summarization based on latent semantic analysis to enhance arabic documents clustering [article]

Hanane Froud, Abdelmonaime Lachkar, Said Alaoui Ouatik
2013 arXiv   pre-print
In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity  ...  Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present  ...  It may impact positively or negatively on the accuracy of any Text Mining tasks; therefore the choice of the preprocessing approaches will lead by necessity to the improvement of any Text Mining tasks  ... 
arXiv:1302.1612v1 fatcat:hru4mls22vdwnic5kvuhhvirg4

Unsupervised learning of Arabic stemming using a parallel corpus

Monica Rogati, Scott McCarley, Yiming Yang
2003 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03  
Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.  ...  Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component  ...  Acknowledgements We would like to thank the reviewers for their helpful observations and for identifying Arabic misspellings.  ... 
doi:10.3115/1075096.1075146 dblp:conf/acl/RogatiMY03 fatcat:xaccjo7kejhqxp5fa6pgu4olx4
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