Extracting Parallel Paragraphs and Sentences from English-Persian Translated Documents
Identifieur interne : 000476 ( Main/Exploration ); précédent : 000475; suivant : 000477Extracting Parallel Paragraphs and Sentences from English-Persian Translated Documents
Auteurs : Sadegh Rasooli [Iran] ; Omid Kashefi [Iran] ; Behrouz Minaei-Bidgoli [Iran]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: The task of sentence and paragraph alignment is essential for preparing parallel texts that are needed in applications such as machine translation. The lack of sufficient linguistic data for under-resourced languages like Persian is a challenging issue. In this paper, we proposed a hybrid sentence and paragraph alignment model on Persian-English parallel documents based on simple linguistic features as well as length similarity between sentences and paragraphs of source and target languages. We apply a small bilingual dictionary of Persian-English nouns, punctuation marks, and length similarity as alignment metrics. We combine these features in a linear model and use genetic algorithm to learn the linear equation weights. Evaluation results show that the extracted features improve the baseline model which is only a length-based one.
Url:
DOI: 10.1007/978-3-642-25631-8_52
Affiliations:
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The lack of sufficient linguistic data for under-resourced languages like Persian is a challenging issue. In this paper, we proposed a hybrid sentence and paragraph alignment model on Persian-English parallel documents based on simple linguistic features as well as length similarity between sentences and paragraphs of source and target languages. We apply a small bilingual dictionary of Persian-English nouns, punctuation marks, and length similarity as alignment metrics. We combine these features in a linear model and use genetic algorithm to learn the linear equation weights. 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