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Feature-based similarity method for aligning the Malay and English news documents


Nasharuddin, Nurul Amelina and Abdullah, Muhamad Taufik and Azman, Azreen and Abdul Kadir, Rabiah and Herrera-Viedma, Enrique (2013) Feature-based similarity method for aligning the Malay and English news documents. International Journal of Computers and Technology, 11 (4). pp. 2410-2421. ISSN 2277-3061


Corpus-based translation approach can be used to obtain reliable translation knowledge in addition to the use of dictionaries or machine translation. But the availability of such corpus is very limited especially for the low-resources languages. Many works have been reported for the alignments of multilingual documents especially among the European languages, but less focusing on the languages with less linguistics resources. One of the challenges is to align the available multilingual documents for the creation of comparable corpus for these kinds of languages. This article describes an alignment method that utilized the statistical features of the documents such as the documents’ titles, texts of the contents, and also the named entities present in each document. This method will be focusing on the English and Malay news documents, in which in which the Malay language is considered as a low-resource language. Source and target documents were then compared in a pair. Accuracy, precision, and recall measurements were used in evaluating the results with the inclusion of three relevance scales; Same story, Shared aspect and Unrelated, to assess the alignment pairs. The results indicate that the method performed well in aligning the news documents with the accuracy of 96% and average precision of 81%.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
Publisher: IJCT Foundation
Keywords: Document alignment; Feature-based method; Algorithm; Malay text processing; Corpus-based information retrieval
Depositing User: Nida Hidayati Ghazali
Date Deposited: 06 Feb 2015 06:54
Last Modified: 07 Dec 2015 03:44
URI: http://psasir.upm.edu.my/id/eprint/30693
Statistic Details: View Download Statistic

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