Probabilistic neural network based english-arabic sentence alignment

Authors:
Mohamed Abdel Fattah;Fuji Ren;Shingo Kuroiwa
Affiliations:
Faculty of Engineering, University of Tokushima, Tokushima, Japan;Faculty of Engineering, University of Tokushima, Tokushima, Japan;Faculty of Engineering, University of Tokushima, Tokushima, Japan
Venue:
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Year:
2006

Citing 3
Cited 3

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Abstract

In this paper, we present a new approach to align sentences in bilingual parallel corpora based on a probabilistic neural network (P-NNT) classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually aligned training data was used to train the probabilistic neural network. Another set of data was used for testing. Using the probabilistic neural network approach, an error reduction of 27% was achieved over the length based approach when applied on English-Arabic parallel documents.