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We present a summary of the first shared task on automatic text correction for Ara-bic text. The shared task received 18 systems submissions from nine teams in six countries and represented a diversity of approaches. Our report includes an overview of the QALB corpus which was the source of the datasets used for training and evaluation , an overview of(More)
We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction(More)
We present a summary of QALB-2015, the second shared task on automatic text correction of Arabic texts. The shared task extends QALB-2014, which focused on correcting errors in Arabic texts produced by native speakers of Arabic. The competition this year, in addition to native data, includes texts produced by learners of Arabic as a foreign language. The(More)
We demonstrate a web-based, language-independent annotation framework used for manual correction of a large Arabic corpus. Our framework provides intuitive interfaces for annotating text and managing the annotation process. We describe the details of both the annotation and the administration interfaces as well as the back-end engine. We also show how this(More)
In this paper, deep learning framework is proposed for text sentiment classification in Arabic. Four different architectures are explored. Three are based on Deep Belief Networks and Deep Auto Encoders, where the input data model is based on the ordinary Bag-of-Words, with features based on the recently developed Arabic Sentiment Lexicon in combination with(More)
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