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The Quran is a significant religious text, followed by the 1.5 billion believers of the Islamic faith worldwide. The text dates to 610–632 CE and is written in Quranic Arabic, the direct ancestor language of modern standard Arabic in use today. This paper presents the Quranic Arabic Dependency Treebank (QADT) and reports on the approaches and(More)
The Quranic Arabic Corpus (http://corpus.quran.com) is an annotated linguistic resource with multiple layers of annotation including morphological segmentation, part-of-speech tagging, and syntactic analysis using dependency grammar. The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year old(More)
The Quranic Arabic Dependency Treebank (QADT) is part of the Quranic Arabic Corpus (http://corpus.quran.com), an online linguistic resource organized by the University of Leeds, and developed through online collaborative annotation. The website has become a popular study resource for Arabic and the Quran, and is now used by over 1,500 researchers and(More)
SemEval-2014 Task 6 aims to advance semantic parsing research by providing a high-quality annotated dataset to compare and evaluate approaches. The task focuses on contextual parsing of robotic commands, in which the additional context of spatial scenes can be used to guide a parser to control a robot arm. Six teams submitted systems using both rule-based(More)
In this paper, we describe and compare two statistical parsing approaches for the hybrid dependency-constituency syntactic representation used in the Quranic Arabic Tree-bank (Dukes and Buckwalter, 2010). In our first approach, we apply a multi-step process in which we use a shift-reduce algorithm trained on a pure dependency prepro-cessed version of the(More)
The Quranic Arabic Corpus (http://corpus.quran.com) is a collaboratively constructed linguistic resource initiated at the University of Leeds, with multiple layers of annotation including part-of-speech tagging, morphological segmentation (Dukes & Habash, 2010) and syntactic analysis using dependency grammar (Dukes & Buckwalter, 2010). The motivation behind(More)
We review a range of Artificial Intelligence and Corpus Linguistics research at Leeds University on Arabic and the Quran, which has produced a range of software and corpus datasets for research on Modern Standard Arabic and more recently Quranic Arabic .Our work on Quranic Arabic corpus linguistics has attracted widespread interest, not only from Arabic(More)