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Statistical Machine Translation as a Grammar Checker for Persian Language

Authors:
Nava Ehsan
Heshaam Faili

Keywords: Natural Language Processing, Syntactic Error, Statistical Machine Translation, Grammar Checker, Persian Language

Abstract:
Existence of automatic writing assistance tools such as spell and grammar checker/corrector can help in increasing electronic texts with higher quality by removing noises and cleaning the sentences. Different kinds of errors in a text can be categorized into spelling, grammatical and real-word errors. In this article, the concepts of an automatic grammar checker for Persian (Farsi) language, is explained. A statistical grammar checker based on phrasal statistical machine translation (SMT) framework is proposed and a hybrid model is suggested by merging it with an existing rule-based grammar checker. The results indicate that these two approaches are complimentary in detecting and correcting syntactic errors, although statistical approach is able to correct more probable errors. The state-of-the-art results on Persian grammar checking are achieved by using the hybrid model. The obtained recall is about 0.5 for correction and about 0.57 for detection with precision about 0.63.

Pages: 20 to 26

Copyright: Copyright (c) IARIA, 2011

Publication date: June 19, 2011

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-139-7

Location: Luxembourg City, Luxembourg

Dates: from June 19, 2011 to June 24, 2011