Extracting N-gram terms collocation from tagged Arabic corpus

Information Extraction (IE) is one of the most important Natural Language Processing (NLP) applications, which extracts information such as Named-Entities (NE) and collocation of terms from the corpus. Collocation is a sequence of terms that co-occur together in the corpus. In Arabic Information Extraction, there are many problems because of the complex of Arabic´s grammar and ambiguity. In general, in linguistics research, the more efficient corpus is the one annotated by Part of Speech Tagging (POST). In this paper, we propose a prototype that extracts collocation of N-gram words (from 2-6 gram) based on the sequence of POST from Arabic Quran corpus. This approach extracts the collocation of N-gram words by matching the input structured pattern of Arabic language versus the Part of Speech Tagging of Quran corpus. The system enables users to select a sequence of tags (2-6 gram) and scope of the corpus source (whole Quran Corpus or specific Surah). To show how the system is beneficial for linguistic research, a set of experiments has been conducted in different scenarios.