A Study of Association Measures and their Combination for Arabic MWT Extraction
This work addresses a domain-specific problem for Arabic natural language processing applications, but it is incremental as it builds on existing methods for other languages.
The paper tackles the problem of automatic Multi-Word Term extraction for Arabic, a language with limited prior studies, by proposing a hybrid method that combines linguistic and statistical approaches; experimental results show that their NLC-value measure outperforms existing competitors in precision for bi-grams and tri-grams.
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.