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 and syntactic analysis using dependency grammar (Dukes & Buckwalter, 2010). The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year-old central religious text of Islam. This project contrasts with other Arabic treebanks by providing a deep linguistic model based on the historical traditional grammar known as i'r b ( ). By adapting this well-known canon of Quranic grammar into a familiar tagset, it is possible to encourage online annotation by Arabic linguists and Quranic experts.This article presents a new approach to linguistic annotation of an Arabic corpus: online supervised collaboration using a multi-stage approach. The different stages include automatic rule-based tagging, initial manual verification, and online supervised collaborative proofreading. A popular website attracting thousands of visitors per day, the Quranic Arabic Corpus has approximately 100 unpaid volunteer annotators each suggesting corrections to existing linguistic tagging. To ensure a high-quality resource, a small number of expert annotators are promoted to a supervisory role, allowing them to review or veto suggestions made by other collaborators. The Quran also benefits from a large body of existing historical grammatical analysis, which may be leveraged during this review. In this paper we evaluate and report on the effectiveness of the chosen annotation methodology. We also discuss the unique challenges of annotating Quranic Arabic online and describe the custom linguistic software used to aid collaborative annotation.

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 and statistical methods. The best performing (hybrid) system scored 92.5% and 90.5% for parsing with and without spatial context. However, the best performing statistical system scored 87.35% and 60.84% respectively, indicating that generalized understanding of commands given to a robot remains challenging, despite the fixed domain used for the task. 'Move the pyramid on the blue cube on the gray one.' Figure 1: Example scene with a contextual spatial command from the Robot Commands Treebank.

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