Arabic Information Retrieval: Techniques, tools and challenges

Al-Maimani, Maqbool; Naamany, Ahmed Al; Bakar, Ahmed Zaki Abu

doi:10.1109/ieeegcc.2011.5752576

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…The Arabic language is rich in vocabulary, a single root composed of three to five letters can generate enormous number of words with different meaning [10]. According to [8], Arabic has an estimated of sixty billion words derived from about 10,000 roots, making it a highly derivational language [11]. For example, the Arabic root ‫"علم"‬ can generate many words, such as ‫"تعليم"‬ ‫"عالم",‬ ‫,"معلم",‬ and ‫.…”

Section: Arabic Language Characteristicsmentioning

confidence: 99%

“…May be this is because Arabic stop words can be combined with prefixes or suffixes, such as the word ‫"عند"‬ which can appear as ‫"عندهم‬ " , or " ‫عنده‬ " Which represent another challenge [8]. Therefore, to improve Arabic IR, stop word list need to include all affix possibilities, and to include stop words used in different Arab countries [11]. [16] investigated and found that the best results they got are obtained by combining Khoja stop word list with [17] stop word list.…”

Section: Preprocessing and Text Normalizationmentioning

confidence: 99%

“…Light-based stemming is a simple approach that involves removing certain prefixes or suffixes from words [11]. [6] proposed several light stemmers, called light 1, 2, 3, and 8, which remove specific prefixes or suffixes from words as shown in Table 2.…”

Section: Light Based Stemmingmentioning

confidence: 99%

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Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

Yaseen,

Al-Khulaidi

2023

jast

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Arabic information retrieval is challenging due to the language's complex morphology and syntax. Preprocessing and stemming improve the accuracy and efficiency of Arabic information retrieval. This paper provides a comprehensive analysis of the existing literature on Arabic preprocessing and stemming techniques. The paper identifies the limitations and challenges of these techniques. The paper emphasizes the importance of preprocessing and stemming and underscores the need for further research to improve Arabic information retrieval. This study evaluates ten stemmers on a public dataset. The results show that root-based stemmers: Lucene, and khoja got the highest reduction rate 90.9%, and 85% respectively. The results emphasize that root-based stemmers have good conflating ability for similar terms, while light-based stemmers under-stem similar terms.

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Section: Arabic Language Characteristicsmentioning

confidence: 99%

Section: Preprocessing and Text Normalizationmentioning

confidence: 99%

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Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

Yaseen,

Al-Khulaidi

2023

jast

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“…To overcome these problems, the researchers suggest the use of query expansion techniques to enhance the user query automatically. Semantically enhancing query expansion improves AIR effectiveness and helps users locate the required information [5].…”

Section: Problem Statement and Objectivesmentioning

confidence: 99%

Semantically enhanced pseudo relevance feedback for Arabic information retrieval

Atwan

Mohd

Rashaideh

et al. 2015

Journal of Information Science

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The conventional information retrieval (IR) framework consists of four primary phases, namely, pre-processing, indexing, querying and retrieving results. Some phases of the current Arabic IR (AIR) framework have several drawbacks. This research aims to enhance an AIR by improving the processes in a conventional IR framework. We introduce an enhanced stop-word list in the pre-processing level and investigate several Arabic stemmers. In addition, an Arabic WordNet was utilized in the corpus and query expansion levels. We also adopted semantic information for the Pseudo Relevance Feedback. The enhanced Arabic IR framework was built and evaluated using TREC 2001 data. The technique of using the Arabic WordNet to build a semantic relationship between query and corpus in two levels, that is, the corpus and query levels, is a new one. The enhanced AIR framework demonstrated an improvement by 49% in terms of mean average precision, with an increase of 7.3% in recall compared with the baseline framework.

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“…Two other approaches are statistical stemming and manual construction of dictionaries; the last one is not efficient. Studies showed that light stemming outperforms aggressive stemming and other stemming approaches [33].…”

Section: Methodsmentioning

confidence: 99%

Combining Different Approaches to Improve Arabic Text Documents Classification

Abuhaiba¹,

Dawoud²

2017

IJISA

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Abstract-The objective of this research is to improve Arabic text documents classification by combining different classification algorithms. To achieve this objective we build four models using different combination methods.The first combined model is built using fixed combination rules, where five rules are used; and for each rule we used different number of classifiers. The best classification accuracy, 95.3%, is achieved using majority voting rule with seven classifiers, and the time required to build the model is 836 seconds.The second combination approach is stacking, which consists of two stages of classification. The first stage is performed by base classifiers, and the second by a meta classifier. In our experiments, we used different numbers of base classifiers and two different meta classifiers: Naï ve Bayes and linear regression. Stacking achieved a very high classification accuracy, 99.2% and 99.4%, using Naï ve Bayes and linear regression as meta classifiers, respectively. Stacking needed a long time to build the models, which is 1963 seconds using naï ve Bayes and 3718 seconds using linear regression, since it consists of two stages of learning.The third model uses AdaBoost to boost a C4.5 classifier with different number of iterations. Boosting improves the classification accuracy of the C4.5 classifier; 95.3%, using 5 iterations, and needs 1175 seconds to build the model, while the accuracy is 99.5% using 10 iterations and requires 1966 seconds to build the model.The fourth model uses bagging with decision tree. The accuracy is 93.7% achieved in 296 seconds when using 5 iterations, and 99.4% when using 10 iteration requiring 471 seconds. We used three datasets to test the combined models: BBC Arabic, CNN Arabic, and OSAC datasets. The experiments are performed using Weka and RapidMiner data mining tools. We used a platform of Intel Core i3 of 2.2 GHz CPU with 4GB RAM.The results of all models showed that combining classifiers can effectively improve the accuracy of Arabic text documents classification.

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Arabic Information Retrieval: Techniques, tools and challenges

Cited by 9 publications

References 11 publications

Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

Semantically enhanced pseudo relevance feedback for Arabic information retrieval

Combining Different Approaches to Improve Arabic Text Documents Classification

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