Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Finding english and translated Arabic documents similarities using GHSOM

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Selamat, A. ; Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai ; Ismail, H.H.

The idea of finding similar news across Arabic and English sources is that to provide the audience with multiple views of the broadcasted news because reading the news from a single source may not always reflects on what happening around the world due different background, cultures and opinions of the readers and writers. To achieve this goal there are many techniques have been used to cluster the documents with similar themes. In this paper, we analyze the similarity of the views on the news written in the news translations form Arabic and English texts using self-organizing map (SOM). However, we have found there are some difficulties in SOM that affect its performance. In order to improve the problems of performance, we have used a growing hierarchical self-organizing map (GHSOM). The main advantage of such a mapping is the ease by which a user gains an idea regarding the structure of the data by analyzing the map. Thousands of news documents have been collected from Arabic and English news sources from the Web in order to train both algorithms. Form experiments, the results show that using GHSOM is better in terms of clustering documents with the same opinions.

Published in:

Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on

Date of Conference:

13-15 May 2008