Cross domains Arabic named entity recognition system

Handle URI:
http://hdl.handle.net/10754/618211
Title:
Cross domains Arabic named entity recognition system
Authors:
Al-Ahmari, S. Saad; Abdullatif Al-Johar, B.
Abstract:
Named Entity Recognition (NER) plays an important role in many Natural Language Processing (NLP) applications such as; Information Extraction (IE), Question Answering (QA), Text Clustering, Text Summarization and Word Sense Disambiguation. This paper presents the development and implementation of domain independent system to recognize three types of Arabic named entities. The system works based on a set of domain independent grammar-rules along with Arabic part of speech tagger in addition to gazetteers and lists of trigger words. The experimental results shown, that the system performed as good as other systems with better results in some cases of cross-domains corpora. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
S. Saad Al-Ahmari and B. Abdullatif Al-Johar " Cross domains Arabic named entity recognition system ", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111I (July 11, 2016); doi:10.1117/12.2240887; http://dx.doi.org/10.1117/12.2240887
Publisher:
SPIE-Intl Soc Optical Eng
Journal:
First International Workshop on Pattern Recognition
Conference/Event name:
First International Workshop on Pattern Recognition
Issue Date:
11-Jul-2016
DOI:
10.1117/12.2240887
Type:
Conference Paper
Additional Links:
http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2240887
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Ahmari, S. Saaden
dc.contributor.authorAbdullatif Al-Johar, B.en
dc.date.accessioned2016-08-10T12:27:00Z-
dc.date.available2016-08-10T12:27:00Z-
dc.date.issued2016-07-11-
dc.identifier.citationS. Saad Al-Ahmari and B. Abdullatif Al-Johar " Cross domains Arabic named entity recognition system ", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111I (July 11, 2016); doi:10.1117/12.2240887; http://dx.doi.org/10.1117/12.2240887en
dc.identifier.doi10.1117/12.2240887-
dc.identifier.urihttp://hdl.handle.net/10754/618211-
dc.description.abstractNamed Entity Recognition (NER) plays an important role in many Natural Language Processing (NLP) applications such as; Information Extraction (IE), Question Answering (QA), Text Clustering, Text Summarization and Word Sense Disambiguation. This paper presents the development and implementation of domain independent system to recognize three types of Arabic named entities. The system works based on a set of domain independent grammar-rules along with Arabic part of speech tagger in addition to gazetteers and lists of trigger words. The experimental results shown, that the system performed as good as other systems with better results in some cases of cross-domains corpora. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.en
dc.publisherSPIE-Intl Soc Optical Engen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2240887en
dc.rightsCopyright 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en
dc.titleCross domains Arabic named entity recognition systemen
dc.typeConference Paperen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalFirst International Workshop on Pattern Recognitionen
dc.conference.dateMay 11, 2016en
dc.conference.nameFirst International Workshop on Pattern Recognitionen
dc.conference.locationTokyo, Japanen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionComputer Science Department, King Faisal University, Hofuf-Al-Hassa-31982,Kingdom of Saudi Arabiaen
kaust.authorAl-Ahmari, S. Saaden
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