Mining arabic text using soft-matching association rules

Al-Zoghby, Aya M.; Eldin, Ahmed Sharaf; Ismail, Nabil A.; Hamza, Taher

doi:10.1109/icces.2007.4447080

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…El-Halees et al also classified 300 Arabic documents by applying different algorithms such as vector space model (VSM), K-Nearest Neighbor algorithms (KNN), and Naïve Bayes (NB). The accuracy of the classification was 74.41% [6]. The same accuracy was obtained in Al-Zoghby's study which includes CHARM algorithm to classify Arabic text documents from 5524 records [7].…”

Section: Related Worksupporting

confidence: 55%

A Survey of Arabic Text Classification Models

Al-Sbou¹

2018

IJECE

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<p>There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The researche in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.</p>

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Section: Related Worksupporting

confidence: 55%

A Survey of Arabic Text Classification Models

Al-Sbou¹

2018

IJECE

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“…Association Rules [97], [98], [99], [100] 4(4%) Named Entity Recognition [101], [102], [103], [104] 4(4%)…”

Section: Techniquementioning

confidence: 99%

Arabic Text Mining a Systematic Review of the Published Literature 2002-2014

AlMahmoud

Al-Razgan

2015

2015 International Conference on Cloud Computing (ICCC)

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Text Mining is a set of techniques that analyzes large masses of data, extract relations that are unknown beforehand, and provide solutions to help decision-making. Text mining had been used extensively to analyze English text. However, text mining has only been used recently in analyzing Arabic text. As a result the objective of this paper is to present the current state of Arabic text mining. A systematic review has been performed to collect the papers published on the analysis of Arabic text mining. More than one hundred papers were used in our review from different reliable sources, and then they were classified according to their specific domain, and classified again according to the specific techniques used. This paper also provides quantitative analysis of publications according to publication type, year, category, and contributors.

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“…The Arabic language has highly inflectional and derivational difficulties because there are many irregular words (Al-Zoghby et al 2007), and there are many vowel patterns, each defining a grammatical state of the stems. For example, let us consider the root ( ‫ﻛﺘﺐ‬ktb 1 ) 'write' which contains three letters ( ‫‪k‬ﻛـ‬ + ‫‪t‬ﺗـ‬ + ‫ﺏ‬b), to derive all the possible stems from this root we should replace the letters (-‫-ﻉ-+‪f‬ﻑ‬ ς+ ‫ﻝ‬l) of all the vowel patterns with the three letters ( ‫+‪k‬ﻛـ‬ ‫ﺕ‬t+‫ﺏ‬b) of the root respectively, as shown in Table 1.…”

Section: Why the Arabic Language?mentioning

confidence: 99%

A real time Named Entity Recognition system for Arabic text mining

Aljumaily

Martı́nez

Martínez-Fernández

et al. 2011

Lang Resources & Evaluation

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Arabic is the most widely spoken language in the Arab World. Most people of the Islamic World understand the Classic Arabic language because it is the language of the Qur'an. Despite the fact that in the last decade the number of Arabic Internet users (Middle East and North and East of Africa) has increased considerably, systems to analyze Arabic digital resources automatically are not as easily available as they are for English. Therefore, in this work, an attempt is made to build a real time Named Entity Recognition system that can be used in web applications to detect the appearance of specific named entities and events in news written in Arabic. Arabic is a highly inflectional language, thus we will try to minimize the impact of Arabic affixes on the quality of the pattern recognition model applied to identify named entities. These patterns are built up by processing and integrating different gazetteers, from DBPedia (http://dbpedia.org/About, 2009) to GATE (A general architecture for text engineering, 2009) and ANERGazet

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Mining arabic text using soft-matching association rules

Cited by 11 publications

References 8 publications

A Survey of Arabic Text Classification Models

A Survey of Arabic Text Classification Models

Arabic Text Mining a Systematic Review of the Published Literature 2002-2014

A real time Named Entity Recognition system for Arabic text mining

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