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Automatic Multi-Word Term (MWT) extraction is a very important issue to manyapplications, such as information retrieval, question answering, and textcategorization. Although many methods have been used for MWT extraction inEnglish and other European languages, few studies have been applied to Arabic.In this paper, we propose a novel, hybrid method which combines linguistic andstatistical approaches for Arabic Multi-Word Term extraction. The maincontribution of our method is to consider contextual information and bothtermhood and unithood for association measures at the statistical filteringstep. In addition, our technique takes into account the problem of MWTvariation in the linguistic filtering step. The performance of the proposedstatistical measure (NLC-value) is evaluated using an Arabic environment corpusby comparing it with some existing competitors. Experimental results show thatour NLC-value measure outperforms the other ones in term of precision for bothbi-grams and tri-grams.

A Study of Association Measures and their Combination for Arabic MWT Extraction