Paper Info
Title
Adapting a resource-light highly multilingual Named Entity Recognition system to Arabic
Abstract
We present a working Arabic information extraction (IE) system that is used to analyze large volumes of news texts every day to extract the named entity (NE) types person, organization, location, date and number, as well as quotations (direct reported speech) by and about people. The Named Entity Recognition (NER) system was not developed for Arabic, but - instead - a highly multilingual, almost language-independent NER system was adapted to also cover Arabic. The Semitic language Arabic substantially differs from the Indo-European and Finno-Ugric languages currently covered. This paper thus describes what Arabic language-specific resources had to be developed and what changes needed to be made to the otherwise language-independent rule set in order to be applicable to the Arabic language. The achieved evaluation results are generally satisfactory, but could be improved for certain entity types.
Year
Venue
Field
2010
LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
Entity linking,Arabic,Computer science,Semitic languages,Named entity,Speech recognition,Information extraction,Artificial intelligence,Natural language processing,Indirect speech,Named-entity recognition,Linguistics
DocType
Citations 
PageRank 
Conference
6
0.47
References 
Authors
11
4
Name
Order
Citations
PageRank
Wajdi Zaghouani119721.27
Bruno Pouliquen267858.19
Mohamed Ebrahim3574.17
Ralf Steinberger494979.70