  Automatic Multi-Word Term (MWT) extraction is a very important issue to many
applications, such as information retrieval, question answering, and text
categorization. Although many methods have been used for MWT extraction in
English and other European languages, few studies have been applied to Arabic.
In this paper, we propose a novel, hybrid method which combines linguistic and
statistical approaches for Arabic Multi-Word Term extraction. The main
contribution of our method is to consider contextual information and both
termhood and unithood for association measures at the statistical filtering
step. In addition, our technique takes into account the problem of MWT
variation in the linguistic filtering step. The performance of the proposed
statistical measure (NLC-value) is evaluated using an Arabic environment corpus
by comparing it with some existing competitors. Experimental results show that
our NLC-value measure outperforms the other ones in term of precision for both
bi-grams and tri-grams.
