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A real time Named Entity Recognition system for Arabic text mining

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Abstract

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 (http://users.dsic.upv.es/grupos/nle/?file=kop4.php).

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Notes

  1. In this paper the Arabic words are presented using the HSB transliteration schema (Habash et al. 2007) as follows: ء ', آĀ, أ Â, ؤ ŵ, إ Ǎ, ئ ŷ, ا A, ب b, ة ħ, ت t, ث θ, ج j, ح H, خ x, د d, ذ ð,ر r, ز z, س s, ش š, ص S, ض D, ط T, ظ Ď, ع ς, غ γ, ف f, ق q, ك k, ل l, م m, ن n, ه h, و w, ى ý, ي y, َ a, ُ u, ِ i, ً ã, ٌ ũ, ٍ ĩ, ّ  ~ , ْ., ـ _,.

  2. ACE 2004, 2005 Multilingual Training Corpus http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2005T09, http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2006T06.

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Acknowledgments

This work has been partially supported by the Spanish Center for Industry Technological Development (CDTI, Ministry of Industry, Tourism and Trade), through the BUSCAMEDIA Project (CEN-20091026), and also by the Spanish research projects: MA2VICMR: Improving the access, analysis and visibility of the multilingual and multimedia information in web for the Region of Madrid (S2009/TIC-1542), and MULTIMEDICA: Multilingual Information Extraction in Health domain and application to scientific and informative documents (TIN2010-20644-C03-01). The authors would like also to thank the IPSC of the European Commission’s Joint Research Centre for allowing us to include the EMM search engine in our system.

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Authors and Affiliations

  1. Computer Science Department, Carlos III University of Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain

    Harith Al-Jumaily & Paloma Martínez

  2. DAEDALUS – Data, Decisions and Language S.A., Avda. de la Albufera, 321, 28031, Madrid, Spain

    José L. Martínez-Fernández

  3. EC Joint Research Centre, Via E. Fermi, 27549, Ispra, Italy

    Erik Van der Goot

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  1. Harith Al-Jumaily
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  2. Paloma Martínez
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  3. José L. Martínez-Fernández
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  4. Erik Van der Goot
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Correspondence to Harith Al-Jumaily.

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Al-Jumaily, H., Martínez, P., Martínez-Fernández, J.L. et al. A real time Named Entity Recognition system for Arabic text mining. Lang Resources & Evaluation 46, 543–563 (2012). https://doi.org/10.1007/s10579-011-9146-z

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