Automated dictionary construction from arabic corpus for meaningful crime information extraction and document classification

Meshrif Alruily; Aladdin Ayesh; Hussein Zedan

doi:10.1109/CISIM.2010.5643676

Automated dictionary construction from arabic corpus for meaningful crime information extraction and document classification

Meshrif Alruily^*, Aladdin Ayesh, Hussein Zedan

^*Corresponding author for this work

De Montfort University

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

5 Citations (Scopus)

Abstract

Arabic is a very widely spoken language but very few mining tools have been developed to exploit the data that lies within bodies of Arabic text. Thus, this paper presents and then uses three automatic algorithm techniques specifically designed for Arabic. The target domain is crime profiling and the methods involve adaptive dictionary building. The bodies of text are mined for crime type, location and nationality. This work is then validated through three experiments, the results of which show that the techniques developed here are promising.

Original language	English
Title of host publication	2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010
Pages	137-142
Number of pages	6
DOIs	https://doi.org/10.1109/CISIM.2010.5643676
Publication status	Published - 2010
Externally published	Yes
Event	2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010 - Krackow, Poland Duration: 8 Oct 2010 → 10 Oct 2010

Conference

Conference	2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010
Country/Territory	Poland
City	Krackow
Period	8/10/10 → 10/10/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CISIM.2010.5643676

Cite this

Automated dictionary construction from arabic corpus for meaningful crime information extraction and document classification. / Alruily, Meshrif; Ayesh, Aladdin; Zedan, Hussein.
2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010. 2010. p. 137-142 5643676.

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

Alruily, M, Ayesh, A & Zedan, H 2010, Automated dictionary construction from arabic corpus for meaningful crime information extraction and document classification. in 2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010., 5643676, pp. 137-142, 2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010, Krackow, Poland, 8/10/10. https://doi.org/10.1109/CISIM.2010.5643676

@inproceedings{0b30998e1100416fa5340c7968024c45,

title = "Automated dictionary construction from arabic corpus for meaningful crime information extraction and document classification",

abstract = "Arabic is a very widely spoken language but very few mining tools have been developed to exploit the data that lies within bodies of Arabic text. Thus, this paper presents and then uses three automatic algorithm techniques specifically designed for Arabic. The target domain is crime profiling and the methods involve adaptive dictionary building. The bodies of text are mined for crime type, location and nationality. This work is then validated through three experiments, the results of which show that the techniques developed here are promising.",

author = "Meshrif Alruily and Aladdin Ayesh and Hussein Zedan",

year = "2010",

doi = "10.1109/CISIM.2010.5643676",

language = "English",

isbn = "9781424478170",

pages = "137--142",

booktitle = "2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010",

note = "2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010 ; Conference date: 08-10-2010 Through 10-10-2010",

}

TY - GEN

T1 - Automated dictionary construction from arabic corpus for meaningful crime information extraction and document classification

AU - Alruily, Meshrif

AU - Ayesh, Aladdin

AU - Zedan, Hussein

PY - 2010

Y1 - 2010

N2 - Arabic is a very widely spoken language but very few mining tools have been developed to exploit the data that lies within bodies of Arabic text. Thus, this paper presents and then uses three automatic algorithm techniques specifically designed for Arabic. The target domain is crime profiling and the methods involve adaptive dictionary building. The bodies of text are mined for crime type, location and nationality. This work is then validated through three experiments, the results of which show that the techniques developed here are promising.

AB - Arabic is a very widely spoken language but very few mining tools have been developed to exploit the data that lies within bodies of Arabic text. Thus, this paper presents and then uses three automatic algorithm techniques specifically designed for Arabic. The target domain is crime profiling and the methods involve adaptive dictionary building. The bodies of text are mined for crime type, location and nationality. This work is then validated through three experiments, the results of which show that the techniques developed here are promising.

UR - http://www.scopus.com/inward/record.url?scp=78651234960&partnerID=8YFLogxK

U2 - 10.1109/CISIM.2010.5643676

DO - 10.1109/CISIM.2010.5643676

M3 - Published conference contribution

AN - SCOPUS:78651234960

SN - 9781424478170

SP - 137

EP - 142

BT - 2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010

T2 - 2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010

Y2 - 8 October 2010 through 10 October 2010

ER -

Automated dictionary construction from arabic corpus for meaningful crime information extraction and document classification

Abstract

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this