Inproceedings
Book title :
2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI'18)
Adresse :
Rabat, Morocco
ISBN : 978-1-4503-5290-1
Publisher :
Informations
Période : March 2018
Pages : 27-32
Détails
A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection
Imene Boukhalfa Sihem Mostefai Nacira Chekkai
Arabic stemming as a technique of Natural Language Processing is increasingly becoming a significant research domain since Arabic is one of the most challengeing laguages. In this study, a new graph based-approach for stemming in Arabic documents was proposed. Moreover, an evaluation the impact of this stemmer on extrinsic plagiarism detection was elaborated. In this approach, a word is represented by a directed weighted graph having a set of connected components. Each of these components has a specific representation. Then, a stem is selected by comparing the word's representation with a database of 450 stems. This stemmer showed efficiency by improving the detection process of extrinsic plagiarism which is proved by the results obtained.
Mots clés :
Arabic Extrinsic plagiarism detection Graph Natural language processing Stemming
Réf. de citation :
misc-lab-236
DOI :
10.1145/3177148.3180089
Lien :
Texte intégral
ACM :
I. Boukhalfa, S. Mostefai and N. Chekkai. 2018. A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection. In Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI'18), Rabat, Morocco (March 2018), ACM, 27-32. DOI: https://doi.org/10.1145/3177148.3180089.
APA :
Boukhalfa, I., Mostefai, S. & Chekkai, N. (2018, March). A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection. In Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI'18), Rabat, Morocco, ACM, 27-32. DOI: https://doi.org/10.1145/3177148.3180089
IEEE :
I. Boukhalfa, S. Mostefai and N. Chekkai, "A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection". In Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI'18), Rabat, Morocco, ACM, pp. 27-32, March, 2018. DOI: https://doi.org/10.1145/3177148.3180089.
BibTeX :
@inproceedings{misc-lab-236,
author = {Boukhalfa, Imene and Mostefai, Sihem and Chekkai, Nacira},
title = {A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection},
booktitle = {2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI'18)},
location = {Rabat, Morocco},
isbn = {978-1-4503-5290-1},
pages = {27--32},
publisher = {ACM},
year = {2018},
month = {March},
doi = {10.1145/3177148.3180089},
url = {http://doi.acm.org/10.1145/3177148.3180089},
keywords = {Arabic, Extrinsic plagiarism detection, Graph, Natural language processing, Stemming}
}
RIS :
TY  - CONF
TI - A Study of Graph Based Stemmer in Arabic Extrinsic Plagiarism Detection
AU - I. Boukhalfa
AU - S. Mostefai
AU - N. Chekkai
PY - 2018
BT - 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI'18), Rabat, Morocco
SN - 978-1-4503-5290-1
SP - 27
EP - 32
PB - ACM
AB - Arabic stemming as a technique of Natural Language Processing is increasingly becoming a significant research domain since Arabic is one of the most challengeing laguages. In this study, a new graph based-approach for stemming in Arabic documents was proposed. Moreover, an evaluation the impact of this stemmer on extrinsic plagiarism detection was elaborated. In this approach, a word is represented by a directed weighted graph having a set of connected components. Each of these components has a specific representation. Then, a stem is selected by comparing the word's representation with a database of 450 stems. This stemmer showed efficiency by improving the detection process of extrinsic plagiarism which is proved by the results obtained.
KW - Arabic
KW - Extrinsic plagiarism detection
KW - Graph
KW - Natural language processing
KW - Stemming
DO - 10.1145/3177148.3180089
UR - http://doi.acm.org/10.1145/3177148.3180089
ID - misc-lab-236
ER -