Improving arabic text categorization using decision trees

Harrag, Fouzi; El-Qawasmeh, Eyas; Pichappan, Pit

doi:10.1109/ndt.2009.5272214

Cited by 76 publications

(51 citation statements)

References 10 publications

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“…Unlike English language, a limited number of studies had been done for Arabic ATC (Al-Harbi et al, 2008;Darwish and Oard, 2002;Duwiri, 2006;Harrag et al, 2009;Kanaan et al, 2009;Khreisat, 2009;Mesleh, 2008;. Among all of them only (Duwiri, 2006;Kanaan et al, 2009) used Bayesian learning model.…”

Section: Introductionmentioning

confidence: 99%

Statistical Bayesian Learning for Automatic Arabic Text Categorization

Al-Salemi

Aziz

2011

Journal of Computer Science

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Problem statement:The rapid increasing of online Arabic documents necessitated applying Text Categorization techniques that are commonly used for English language to categorize them automatically. The complex morphology of Arabic language and its large vocabulary size make applying these techniques directly difficult and costly in time and effort. Approach: We have investigated Bayesian learning models in order to enhance Arabic ATC. Three classifiers based on Bayesian theorem had been implemented which are Simple Naïve Bayes (NB), Multi-variant Bernoulli Naïve Bayes (MBNB) and Multinomial Naïve Bayes (MNB) models. TREC-2002 Light Stemmer was applied for Arabic stemming. For text representation, Bag-Of-Word and character-level n-gram with the length 3, 4 and 5 are used. In order to reduce the dimensionality of feature space, the following feature selection methods: Mutual Information, Chi-Square statistic, Odds Ratio and GSS-coefficient were used. Conclusion: MBNB classifier outperformed both of NB and MNB classifiers. BOW representation leads to the best classification performance; nevertheless, using character-level n-gram leads to satisfying results for Arabic ATC based on Bayesian learning. Moreover, the use of feature selection methods dramatically increases the categorization performance.

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Section: Introductionmentioning

confidence: 99%

Statistical Bayesian Learning for Automatic Arabic Text Categorization

Al-Salemi

Aziz

2011

Journal of Computer Science

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“…Text collection (TC) is a process to classify the document into groups based on the similarity, and it applies in some applications such as text filtering, web page categories, document organization, and other [11], [13]. The text classification has been become importance due to increasing the amount of data such as stories, news on the internet to facilitate the task of information retrieval when needed [15].…”

Section: Text Classificationmentioning

confidence: 99%

A Method of Automatic Domain Extraction of Text to Facilitate Retrieval of Arabic Documents

Al-Maghasbeh¹,

Hamzah²

2018

ijacsa

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Abstract-Arabic content on the internet has increased over the web because of the growth of the number of Arabic persons who use the internet in the world. Accordingly, this study introduces an automatic approach of domain extraction of information retrieval from these contents based on text classification. Text classification process makes the searching domain specific to facilitate the searching process. This paper discusses how to enhance the capacity of information retrieval in Arabic documents by classifying the unlabelled Arabic text automatically by using text classification algorithms. The classification of documents and texts is an important field in computer science and information retrieval. It aims at enhancing the retrieval process by identifying the searching-domain of retrieval systems.

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“…Decision trees classification approach and the effect of feature selection on the predictive accuracy were applied to the problem of Arabic text mining in the work of [10]. Specifically, the authors have compared ID3 algorithm with the known statistical method of Naive Bayes for two Arabic data sets collected from Arabian scientific encyclopedia (Hal Taalam) and Prophet Mohammad"s "Peace Be Upon him" Hadeeths "Nine Hadeeth Book".…”

Section: Literature Reviewmentioning

confidence: 99%

“…Naï ve Bayes [9], Support Vector Machine (SVM) and Decision Tree [10]. However, researchers conclude that Arabic text classification is a very challenging task due to language complexity.…”

Section: Introductionmentioning

confidence: 99%

EMCAR: Expert Multi Class Based on Association Rule

Hadi

2013

IJMECS

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Abstract-Several experimental studies revealed that expert systems have been successfully applied in real world domains such as medical diagnoses, traffic control, and many others. However, one of the major drawbacks of classic expert systems is their reliance on human domain experts which require time, care, experience and accuracy. This shortcoming also may result in building knowledge bases that may contain inconsistent rules or contradicting rules. To treat the abovementioned we intend to propose and develop automated methods based on data mining called Associative Classification (AC) that can be easily integrated into an expert system to produce the knowledge base according to hidden correlations in the input database. The methodology employed in the proposed expert system is based on learning the rules from the database rather than inputting the rules by the knowledge engineer from the domain expert and therefore, care and accuracy as well as processing time are improved. The proposed automated expert system contains a novel learning method based on AC mining that has been evaluated on Islamic textual data according to several evaluation measures including recall, precision and classification accuracy. Furthermore, five different classification approaches: Decision trees (C4.5, KNN, SVM, MCAR and NB) and the proposed automated expert system have been tested on the Islamic data set to determine the suitable method in classifying Arabic texts.

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Improving arabic text categorization using decision trees

Cited by 76 publications

References 10 publications

Statistical Bayesian Learning for Automatic Arabic Text Categorization

Statistical Bayesian Learning for Automatic Arabic Text Categorization

A Method of Automatic Domain Extraction of Text to Facilitate Retrieval of Arabic Documents

EMCAR: Expert Multi Class Based on Association Rule

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