Combining Indexing Units for Arabic Information Retrieval

Using either stems or roots as index terms offered considerable performance to Arabic Information Retrieval IR systems compared to the use of surface words for indexing. Many comparative works tried to find out the best from these two indexing approaches but until then, no of the two methods widely overtook the other. Each of the two index types performed better under different test circumstances in terms of recall and precision. In this paper, the authors propose a hybrid approach combining the two indexing units in a way they take the advantages from both of them and try to overcome their shortcomings. Then, based on some combining techniques, the authors assign a weight for each indexing unit and try to find out the best weighting values.

[1]  Qasem A. Al-Radaideh,et al.  Benchmarking and assessing the performance of Arabic stemmers , 2011, J. Inf. Sci..

[2]  Ibrahim Bounhas,et al.  ArabOnto: experimenting a new distributional approach for building Arabic ontological resources , 2011, Int. J. Metadata Semant. Ontologies.

[3]  Amer Al-Badarneh,et al.  A comparison study of some Arabic root finding algorithms , 2010 .

[4]  Ophir Frieder,et al.  On arabic search: improving the retrieval effectiveness via a light stemming approach , 2002, CIKM '02.

[5]  W. Bruce Croft,et al.  Linear feature-based models for information retrieval , 2007, Information Retrieval.

[6]  Feng Zhang,et al.  Research on measuring semantic correlation based on the Wikipedia hyperlink network , 2013, 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS).

[7]  Riyad Al-Shalabi.  Pattern-based Stemmer for Finding Arabic Roots , 2005 .

[8]  A. Badran,et al.  Building a lexical semantic resource for Arabic morphological Patterns , 2013, 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA).

[9]  Narjès Bellamine Ben Saoud,et al.  Experimenting a discriminative possibilistic classifier with reweighting model for Arabic morphological disambiguation , 2015, Comput. Speech Lang..

[10]  M. Hadni,et al.  A new and efficient stemming technique for Arabic Text Categorization , 2012, 2012 International Conference on Multimedia Computing and Systems.

[11]  Kazem Taghva,et al.  Arabic stemming without a root dictionary , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[12]  Narjès Bellamine Ben Saoud,et al.  Improving Arabic Texts Morphological Disambiguation Using a Possibilistic Classifier , 2014, NLDB.

[13]  Mohand Boughanem,et al.  Aggregated search: A new information retrieval paradigm , 2014, CSUR.

[14]  Feng Luo,et al.  Ontology construction for information selection , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[15]  Leah S. Larkey,et al.  Arabic Information Retrieval at UMass in TREC-10 , 2001, TREC.

[16]  Feng Zhang,et al.  Research on Measuring Semantic Correlation Based on the Wikipedia Hyperlink Network , 2013, Int. J. Softw. Innov..

[17]  Sameh H. Ghwanmeh,et al.  Enhanced Algorithm for Extracting the Root of Arabic Words , 2009, 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization.

[18]  Douglas W. Oard,et al.  Term selection for searching printed Arabic , 2002, SIGIR '02.

[19]  D. Frank Hsu,et al.  Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval , 2005, Information Retrieval.

[20]  Fernando Diaz,et al.  Learning to aggregate vertical results into web search results , 2011, CIKM '11.

[21]  Fredric C. Gey,et al.  Building an Arabic Stemmer for Information Retrieval , 2002, TREC.

[22]  Wiem Lahbib,et al.  Arabic Domain Terminology Extraction: A Literature Review - (Short Paper) , 2014, OTM Conferences.

[23]  Suleiman H. Mustafa.  Combining n-Grams and Stemming for Arabic Word-Based Inexact Matching and Term Conflation , 2005, J. Inf. Knowl. Manag..

[24]  Adel M. Alimi,et al.  The Multi-Agents Architecture for Emotion Recognition: Case of Information Retrieval System , 2014, Int. J. Softw. Innov..

[25]  Ibrahim Bounhas,et al.  Arabic Cross-Language Information Retrieval , 2016, ACM Trans. Asian Low Resour. Lang. Inf. Process..

[26]  R. Al Shalabi,et al.  New approach for extracting Arabic roots , 2003 .

[27]  Riyad Al-Shalabi,et al.  Building an effective rule-based light stemmer for Arabic language to inprove search effectiveness , 2008, 2008 International Conference on Innovations in Information Technology.

[28]  Rashedur M. Rahman,et al.  Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation , 2016, Int. J. Softw. Innov..