Effective unsupervised Arabic word stemming : towards an unsupervised radicals extraction
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 9, Issue 6 (30 Nov. 2012)7 p.
Publisher
Publication Date
2012-11-30
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Languages & Comparative Literature
Information Technology and Computer Science
Topics
Abstract EN
This paper presents a new totally unsupervised and 90% effective stemming approach for classical Arabic.
This stemming is meant to be a preparatory step to an unsupervised root (i.e., radicals) extraction.
As a learning input, our stemming system requires no linguistic knowledge but a plain classical Arabic text.
Once the learning input analyzed, our stemming system is able to extract the strongest segment of a given length, namely the stem.
We start by a definition of the targeted stem, then, we show how our system performs about 90 % true positives after a leaning of less than 15000 words.
Unlike the other unsupervised approaches, ours does not suppose the perfectness of the input text and deals efficiently with the eventual (practically very frequent) misspellings.
The test corpus we have used is an ultimate reference in the classical Arabic and its labeling has been rigorously done by a team of experts.
American Psychological Association (APA)
Khorsi, Ahmad. 2012. Effective unsupervised Arabic word stemming : towards an unsupervised radicals extraction. The International Arab Journal of Information Technology،Vol. 9, no. 6.
https://search.emarefa.net/detail/BIM-305088
Modern Language Association (MLA)
Khorsi, Ahmad. Effective unsupervised Arabic word stemming : towards an unsupervised radicals extraction. The International Arab Journal of Information Technology Vol. 9, no. 6 (Nov. 2012).
https://search.emarefa.net/detail/BIM-305088
American Medical Association (AMA)
Khorsi, Ahmad. Effective unsupervised Arabic word stemming : towards an unsupervised radicals extraction. The International Arab Journal of Information Technology. 2012. Vol. 9, no. 6.
https://search.emarefa.net/detail/BIM-305088
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references.
Record ID
BIM-305088