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First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition
| 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
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
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