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First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition

by Fadl Dahan, Ameur Touir, Hassan Mathkour
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 7
Year of Publication: 2015
Authors: Fadl Dahan, Ameur Touir, Hassan Mathkour
10.5120/ijca2015905397

Fadl Dahan, Ameur Touir, Hassan Mathkour . First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition. International Journal of Computer Applications. 123, 7 ( August 2015), 37-40. DOI=10.5120/ijca2015905397

@article{ 10.5120/ijca2015905397,
author = { Fadl Dahan, Ameur Touir, Hassan Mathkour },
title = { First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 7 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number7/21974-2015905397/ },
doi = { 10.5120/ijca2015905397 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:04.222828+05:30
%A Fadl Dahan
%A Ameur Touir
%A Hassan Mathkour
%T First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 7
%P 37-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Name Entity Recognition (NER) is an important process used for several type of applications such as Information Extraction, Information Retrieval, Question Answering, text clustering, etc. It is intended to identify and classify name entities from a given text. NER is performed by using a rule-based approach that relies on human intuitive or machine learning methods such as Hidden Markov Model (HMM), Maximum Entropy (ME), and Decision tree (DT). In this paper, we describe a model based on the first order HMM to recognize name entity in the Arabic language. The model is based on stemming process that solves Arabic's inflection problem and ambiguity. To the best of our knowledge, no work uses this approach for the Arabic language has been reported.

References
  1. R. Grishman and B. Sundheim, “Message Understanding Conference - 6: A Brief History”. COLING-96.
  2. A. Borthwick, "A Maximum Entropy Approach to Named Entity Recognition". New York University, September, 1999.
  3. Y. Benajiba, P. Rosso, J. Miguel and B. Ruiz "ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy". Proceeding of 8th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing, Mexico City,Mexico, February , 2007. Vol 4394/2007,pp.143-153.
  4. K. Shaalan and H. Raza, "Person Name Entity Recognition for Arabic". Proceedings of the 5th Workshop on Important Unresolved Matters. Prague, Czech Republic, June 2007. pp. 17–24.
  5. Marie-Francine Moens, "Information Extraction: Algorithms and Prospects in a Retrieval Context", 1st edition Springer, 2006.
  6. Daniel M. Bikel, Scott Miller, Richard Schwartz, and Ralph Weischedel, "Nymble: A high performance learning name-finder". Proceedings of the 5th Conference on Applied Natural Language Processing, pages 194–201.
  7. J. Maloney and M. Niv, "TAGARAB: A Fast, Accurate Arabic Name Using High-Precision Morphological Analysis". Proceeding of the Workshop on Computational Approaches to Semitic Language, August 1998. pp. 8-15.
  8. S. Abuleil, "Extracting Names From Arabic Text For Question-Answering Systems". Proceedings of Coupling approaches, coupling media and coupling languages for information retrieval (RIAO2004), Avignon, France. pp. 638- 647.
  9. S. Mesfar, "Name Entity Recognition for Arabic Using Syntactic Grammars". Proceeding 12th International Conference on Applications of Natural Language to Information Systems, NLDB, Paris, France, June, 2007. Vol. 4592/2007, pp.305-316.
  10. Y. Benajiba and P. Rosso, "ANERsys 2.0: Conquering the NER Task for Arabic Language by combining the Maximum Entropy with POS-tag information". Proceeding of the 3rd Indian International Conference on Artificial Intelligence (IICAI-0 December 17-19 2007. pp.1814-1823.
  11. Y. Benajiba. Natural Language Engineering Lab http://www.dsic.upv.es/~ybenajiba.
Index Terms

Computer Science
Information Sciences

Keywords

Hidden Markov Model (HMM) Name Entity Recognition (NER) Bigram Model.

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