Supervised collaboration for syntactic annotation of Quranic Arabic

Language Resources and Evaluation (Impact Factor: 0.62). 03/2013; 47(1):1-30. DOI: 10.1007/s10579-011-9167-7


The Quranic Arabic Corpus ( 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 and Habash 2010) and syntactic analysis using dependency grammar (Dukes and Buckwalter 2010). The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400year-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′rāb (إعراب). 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.

KeywordsCollaborative annotation–Arabic–Treebank–Quran–Corpus

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Available from: Eric Atwell
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    • "Little number of research studies exist that are concerned with the production of datasets related to the Holy Quran and they are not comprehensive [2], [3], [4], [5]. For example [6] built semantic Quranic dataset, which include the Quran in several languages. "
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    ABSTRACT: Extracting knowledge from text documents has become one of the main hot topics in the field of Natural Language Processing (NLP) in the era of information explosion. Arabic NLP is considered immature due to several reasons including the low available resources. On the other hand, automatically extracting reliable knowledge from specialized data sources as holy books is considered ultimately a challenging task but of great benefit to all humans. In this context, this paper provides a comprehensive Quranic Dataset as a first part (foundation) of an ongoing research that attempts to lay grounds for approaches and applications to explore the holy Quran. The paper presents the algorithms and approaches that have been designed to extract an aggregative data from massive Arabic text sources including the holy Quran and tightly associated books. Holy Quran text is transferred into structured multi-dimensional data records starting from the chapter level, the word level and then the character level. All these are linked with interpretations and meanings, parsing, translations, intonation roots and stems of words, all from authentic and reliable sources. The final dataset is represented in excel sheets and database records format. Also, the paper presents models of the dataset at all levels. The Quranic dataset presented in this paper was designed to be appropriate for: database, data mining, text mining and Artificial Intelligence applications; it is also designed to serve as a comprehensive encyclopedia of holy Quran and the Quranic Science books.
    Full-text · Article · Jan 2016 · International Journal of Advanced Computer Science and Applications
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    • "" Infoboxes. " The Quranic Arabic Corpus ontology [15] "

    Preview · Article · Jul 2015
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    • "These make dealing with Arabic language a challenging task when applying machine learning and artificial intelligence techniques. Few research studies have considered the Arabic text of Quran [5], [6], [7], [8], instead many studies deal with the translations of the meaning of the words of the holy Quran [9], [10], [11], [12], [13], [14]. Kais and his colleagues have created an open source Quranic corpus [15] using both arabic words as well as translations of these words. "
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    ABSTRACT: The Holy Quran is the reference book for more than 1.6 billion of Muslims all around the world Extracting information and knowledge from the Holy Quran is of high benefit for both specialized people in Islamic studies as well as non-specialized people. This paper initiates a series of research studies that aim to serve the Holy Quran and provide helpful and accurate information and knowledge to the all human beings. Also, the planned research studies aim to lay out a framework that will be used by researchers in the field of Arabic natural language processing by providing a ”Golden Dataset” along with useful techniques and information that will advance this field further. The aim of this paper is to find an approach for analyzing Arabic text and then providing statistical information which might be helpful for the people in this research area. In this paper the holly Quran text is preprocessed and then different text mining operations are applied to it to reveal simple facts about the terms of the holy Quran. The results show a variety of characteristics of the Holy Quran such as its most important words, its wordcloud and chapters with high term frequencies. All these results are based on term frequencies that are calculated using both Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods.
    Full-text · Article · Mar 2015 · International Journal of Advanced Computer Science and Applications
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