ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy

Benajiba, Yassine; Rosso, Paolo; Benedíruiz, José Miguel

doi:10.1007/978-3-540-70939-8_13

Cited by 130 publications

(84 citation statements)

References 13 publications

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“…In (Nadeau and Sekine, 2007) Although rule based approaches proved successful to some extent, most recent NER research focuses on Statistical Learning techniques due to the shortcomings of rule based approaches in terms of coverage and robustness (Nadeau and Sekine, 2007). For example, (Benajiba et al, 2007) proposes an MSA NER system (ANERsys) based on n-grams and maximum entropy. The authors also introduce ANERCorp corpora and ANERGazet gazetteers.…”

Section: Related Workmentioning

confidence: 99%

Named Entity Recognition for Arabic Social Media

Zirikly

Diab

2015

Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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The majority of research on Arabic Named Entity Recognition (NER) addresses the the task for newswire genre, where the language used is Modern Standard Arabic (MSA), however, the need to study this task in social media is becoming more vital. Social media is characterized by the use of both MSA and Dialectal Arabic (DA), with often code switching between the two language varieties. Despite some common characteristics between MSA and DA, there are significant differences between which result in poor performance when MSA targeting systems are applied for NER in DA. Additionally, most NER systems rely primarily on gazetteers, which can be more challenging in a social media processing context due to an inherent low coverage. In this paper, we present a gazetteers-free NER system for Dialectal data that yields an F1 score of 72.68% which is an absolute improvement of ≈ 2 − 3% over a comparable state-ofthe-art gazetteer based DA-NER system.

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Section: Related Workmentioning

confidence: 99%

Named Entity Recognition for Arabic Social Media

Zirikly

Diab

2015

Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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“…The Arabic Khoja's stemmer [23] was adopted for keywords stemming. The system was tested thoroughly to insure correctness of obtained results and then an extensive evaluation have been carried out to compare JAWEB performance with a well established web-based QA system that supports Arabic Language, Ask.com [10], as described in the following section.…”

Section: System Implementationmentioning

confidence: 99%

Development and Evaluation of a Web Based Question Answering System for Arabic Language

Kurdi¹,

Alkhaider²,

Alfaifi³

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…So it assumes that these names are found in a space of 10 words to the left and 10 words to the right of the trigger word. ANERsys which is a NER system built exclusively for Arabic texts based-on n-grams and maximum entropy approach is presented in Benajiba et al (2007). They developed their own corpora and gazetteers to train, evaluate and boost the system.…”

Section: Related Workmentioning

confidence: 99%

“…They obtained an improvement with respect to a baseline results without using any POS-tag information or text segmentation. In Benajiba et al (2010) the authors achieve a significant, highperformance Arabic NER system by using lexical, syntactic and morphological features. In Shaalan and Raza (2008) a system for Arabic NER is presented which consists of two main processing resources: a dictionary of names (whitelist or gazetteer), and a grammar, in the form of regular rules to recognize the Named entities.…”

Section: Related Workmentioning

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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ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy

Cited by 130 publications

References 13 publications

Named Entity Recognition for Arabic Social Media

Named Entity Recognition for Arabic Social Media

Development and Evaluation of a Web Based Question Answering System for Arabic Language

A real time Named Entity Recognition system for Arabic text mining

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