A general framework for multilingual text mining using self-organizing maps

Abdulsamad Al-Marghilani; Husien Zedan; Aladdin Ayesh

A general framework for multilingual text mining using self-organizing maps

Abdulsamad Al-Marghilani^*, Husien Zedan, Aladdin Ayesh

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

1 Citation (Scopus)

Abstract

Arabic is a major and a highly inflected language, and thus requires good stemming for effective text mining. Yet no standard approach to stemming has emerged. This work investigates some of the issues involved in achieving multilingual text mining (MTM). This work is based on Self-Organizing Map (SOM) and uses Arabic/English corpus as the test-bed. Issues related to Arabic/English text mining, stemming and clustering are discussed in this paper. In the authors knowledge there is no significant literature available regarding SOM technique applied to Arabic and English languages text mining.

Original language	English
Title of host publication	Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007
Pages	520-525
Number of pages	6
Publication status	Published - 2007
Event	IASTED International Conference on Artificial Intelligence and Applications, AIA 2007 - Innsbruck, Austria Duration: 12 Feb 2007 → 14 Feb 2007

Conference

Conference	IASTED International Conference on Artificial Intelligence and Applications, AIA 2007
Country/Territory	Austria
City	Innsbruck
Period	12/02/07 → 14/02/07

Keywords

Arabic
Multilingual dictionary
SOM
Stemming
Text mining

Cite this

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title = "A general framework for multilingual text mining using self-organizing maps",

abstract = "Arabic is a major and a highly inflected language, and thus requires good stemming for effective text mining. Yet no standard approach to stemming has emerged. This work investigates some of the issues involved in achieving multilingual text mining (MTM). This work is based on Self-Organizing Map (SOM) and uses Arabic/English corpus as the test-bed. Issues related to Arabic/English text mining, stemming and clustering are discussed in this paper. In the authors knowledge there is no significant literature available regarding SOM technique applied to Arabic and English languages text mining.",

keywords = "Arabic, Multilingual dictionary, SOM, Stemming, Text mining",

author = "Abdulsamad Al-Marghilani and Husien Zedan and Aladdin Ayesh",

year = "2007",

language = "English",

isbn = "9780889866317",

pages = "520--525",

booktitle = "Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007",

note = "IASTED International Conference on Artificial Intelligence and Applications, AIA 2007 ; Conference date: 12-02-2007 Through 14-02-2007",

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TY - GEN

T1 - A general framework for multilingual text mining using self-organizing maps

AU - Al-Marghilani, Abdulsamad

AU - Zedan, Husien

AU - Ayesh, Aladdin

PY - 2007

Y1 - 2007

N2 - Arabic is a major and a highly inflected language, and thus requires good stemming for effective text mining. Yet no standard approach to stemming has emerged. This work investigates some of the issues involved in achieving multilingual text mining (MTM). This work is based on Self-Organizing Map (SOM) and uses Arabic/English corpus as the test-bed. Issues related to Arabic/English text mining, stemming and clustering are discussed in this paper. In the authors knowledge there is no significant literature available regarding SOM technique applied to Arabic and English languages text mining.

AB - Arabic is a major and a highly inflected language, and thus requires good stemming for effective text mining. Yet no standard approach to stemming has emerged. This work investigates some of the issues involved in achieving multilingual text mining (MTM). This work is based on Self-Organizing Map (SOM) and uses Arabic/English corpus as the test-bed. Issues related to Arabic/English text mining, stemming and clustering are discussed in this paper. In the authors knowledge there is no significant literature available regarding SOM technique applied to Arabic and English languages text mining.

KW - Arabic

KW - Multilingual dictionary

KW - SOM

KW - Stemming

KW - Text mining

UR - http://www.scopus.com/inward/record.url?scp=38349172154&partnerID=8YFLogxK

M3 - Published conference contribution

AN - SCOPUS:38349172154

SN - 9780889866317

SP - 520

EP - 525

BT - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007

T2 - IASTED International Conference on Artificial Intelligence and Applications, AIA 2007

Y2 - 12 February 2007 through 14 February 2007

ER -

A general framework for multilingual text mining using self-organizing maps

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