Machine Translation Oriented Syntactic Normalization of Noun Phrases in Arabic (original) (raw)

Abstract

It has been shown that syntactic normalization boosts text summarization and improves both precision and recall in information retrieval. This paper shows that syntactic normalization can also improve the performance of machine translation systems. This paper presents a method for identifying and normalizing the structural variations of the nominal 'Construct State', also known as iDafa, in Arabic. This type of noun phrases has the structure Noun1 Noun2, and is highly frequent in Arabic. The paper (i) describes the structural variations of this construction, (ii) describes the suggested method for identifying its structural variations, and (iii) finally tests the effect of normalizing these variants on the performance of Arabic-to-English machine translation systems. The results showed a 5-point improvement in MT performance. To the author's best knowledge, this is the first attempt for text normalization in Arabic, and its corresponding effect on Arabic MT.

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