Paper Info
Title
A Semantic Annotation Model for Arabic Legal Texts.
Abstract
This paper addresses an issue in the field of Artificial Intelligence and Law. It's concerned with the problem of automatically identifying and semantically annotating normative provisions' categories in Arabic legal texts. This goal has been achieved through the construction of a semantic annotation model, which combines three linguistic resources namely a taxonomy of Arabic normative provisions' categories, an Arabic normative terminological base and a semantic annotation rule base. The performance of the model has been evaluated over a test dataset of Arabic normative texts collected from the Official Gazette of the Republic of Tunisia. Precision, Recall and F-score measures have been used for evaluation. Obtained results are very promising: 96,4% for Precision, 96,06% for Recall and 96,23% for F-score.
Year
DOI
Venue
2016
10.1145/2903220.2903244
SETN
Keywords
Field
DocType
Artificial Intelligence and Law, Natural Language Processing, Semantic Annotation, Information Extraction, Arabic language, Legal Corpora, Normative provisions
Data mining,Artificial intelligence and law,Semantic annotation,Arabic,Normative,Computer science,Information extraction,Natural language processing,Artificial intelligence,The Republic,Recall
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
Ines Berrazega101.01
Rim Faiz29836.23
Asma Bouhafs300.68
Ghassan Mourad400.34