  Bilingual dictionaries are very important in various fields of natural
language processing. In recent years, research on extracting new bilingual
lexicons from non-parallel (comparable) corpora have been proposed. Almost all
use a small existing dictionary or other resources to make an initial list
called the "seed dictionary". In this paper, we discuss the use of different
types of dictionaries as the initial starting list for creating a bilingual
Persian-Italian lexicon from a comparable corpus. Our experiments apply
state-of-the-art techniques on three different seed dictionaries; an existing
dictionary, a dictionary created with pivot-based schema, and a dictionary
extracted from a small Persian-Italian parallel text. The interesting challenge
of our approach is to find a way to combine different dictionaries together in
order to produce a better and more accurate lexicon. In order to combine seed
dictionaries, we propose two different combination models and examine the
effect of our novel combination models on various comparable corpora that have
differing degrees of comparability. We conclude with a proposal for a new
weighting system to improve the extracted lexicon. The experimental results
produced by our implementation show the efficiency of our proposed models.
