Arabic Person Names Recognition by Using a Rule Based Approach

Abstract

Name Entity Recognition is very important task in many natural language processing applications such as; Machine Translation, Question Answering, Information Extraction, Text Summarization, Semantic Applications and Word Sense Disambiguation. Rule-based approach is one of the techniques that are used for named entity recognition to identify the named entities such as a person names, location names and organization names. The recent rule-based methods have been applied to recognize the person names in political domain. They ignored the recognition of other named entity types such as locations and organizations. We have used the rule based approach for recognizing the named entity type (person names) for Arabic. We have developed four rules for identifying the person names depending on the position of name. We have used an in-house Arabic corpus collected from newspaper achieves. The evaluation method that compares the results of the system with the manually annotated text has been applied in order to compute precision, recall and f-measure. In the experiment of this study, the average f-measure for recognizing person names are (92.66, 92.04 and 90.43%) in sport, economic and politic domain respectively. The experimental results showed that our rule-based method achieved the highest f-measure values in sport domain comparing with political and economic domains.

A Comparison between Conditional Random Field and Structured Support Vector Machine for Arabic Named Entity Recognition

“…RENAR outperforms ANERsys 1.0 , ANERsys2.0 in extracting Location NEs when applied to ANERcorp dataset. Aboaoga and Aziz (2013) have suggested a rulebased Arabic NER system that obtains person entities. The system includes sports, politics and political economy domains.…”

Splitting Arabic Texts into Elementary Discourse Units

“…Most research on Arabic NLP resource generation has focused on morphology [Boudlal et al 2011], lexical semantics [Diab et al 2008], and syntactic analysis [Maamouri et al 2010b]. There is also a huge literature on Arabic NLP including shallow and deep syntactic parsing [Ali Mohammed and Omar 2011;Diab et al 2007Diab et al , 2009Green and Manning 2010;Marton et al 2013;Nivre 2007], morphology analysis [Eskander et al 2013;Gridach and Chenfour 2011;Sawalha et al 2013], question answering [Bebajiba et al 2010;Trigui et al 2012], automatic translation [Carpuat et al 2012;Sadat and Mohamed 2013], opinion mining and sentiment analysis [Abdul-Mageed and Diab 2012;Abu-Jbara et al 2013;Mourad and Darwish 2013], and named entity recognition [Aboaoga and Ab-Aziz 2013;Boujelben et al 2013;Darwish 2013].…”

Integrating Semantic Features for Enhancing Arabic Named Entity Recognition

“…The ANERCorp dataset was used to compare our work with previous works. The datasets is divided into 80% as 13 [17], and Kareem Darwish [18].…”

“…The ANER researcher developed system depends on two approaches Ruled Based approach [7,8,9,13,14] or Machine learning (ML) approach [15 -18]. The systems were built using Ruled-Based approach, which depends on linguistic rules for recognizing NEs.…”