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Bilingual dictionaries are very important in various fields of naturallanguage processing. In recent years, research on extracting new bilinguallexicons from non-parallel (comparable) corpora have been proposed. Almost alluse a small existing dictionary or other resources to make an initial listcalled the "seed dictionary". In this paper, we discuss the use of differenttypes of dictionaries as the initial starting list for creating a bilingualPersian-Italian lexicon from a comparable corpus. Our experiments applystate-of-the-art techniques on three different seed dictionaries; an existingdictionary, a dictionary created with pivot-based schema, and a dictionaryextracted from a small Persian-Italian parallel text. The interesting challengeof our approach is to find a way to combine different dictionaries together inorder to produce a better and more accurate lexicon. In order to combine seeddictionaries, we propose two different combination models and examine theeffect of our novel combination models on various comparable corpora that havediffering degrees of comparability. We conclude with a proposal for a newweighting system to improve the extracted lexicon. The experimental resultsproduced by our implementation show the efficiency of our proposed models.

Extracting Bilingual Persian Italian Lexicon from Comparable Corpora Using Different Types of Seed Dictionaries