Samah Meghawry, Abeer Elkorany, Akram Salah and Tarek Elghazaly, Cairo University, Egypt

A considerable interest has been given to Multiword Expression (MWEs) identification and treatment. The identification of MWEs affects the quality of results of different tasks heavily used in natural language processing (NLP) such as parsing and generation. Different approaches for MWEs identification have been applied such as statistical methods which employed as an inexpensive and language independent way of finding co-occurrence patterns. Another approach relays on linguistic methods for identification, which employ information such as part of speech (POS) filters and lexical alignment between languages is also used and produced more targeted candidate lists. This paper presents a framework for extracting Arabic MWEs (nominal or verbal MWEs) for bi-gram using hybrid approach. The proposed approach starts with applying statistical method and then utilizes linguistic rules in order to enhance the results by extracting only patterns that match relevant language rule. The proposed hybrid approach outperforms other traditional approaches.

Multiword expressions (MWEs), Statistical Measures, Part of speech tagging (POS), Nominal MWEs, verbal MWEs.