Arabic is a widely-spoken language with a rich and long history spanning more than fourteen centuries. Yet existing Arabic corpora largely focus on the modern period or lack sufficient diachronic information. We develop a large-scale, historical corpus of Arabic of about 1 billion words from diverse periods of time. We clean this corpus, process it with a morphological analyzer, and enhance it ...

The Arabic language is a collection of spoken dialects with important phonological, morphological, lexical, and syntactic differences, along with a standard written language, Modern Standard Arabic (MSA). Since the spoken dialects are not officially written, it is very costly to obtain adequate corpora to use for training dialect NLP tools such as parsers. In this paper, we address the problem ...

In this paper, we describe an extension to a hybrid machine translation system for handling dialect Arabic, using a decoding algorithm to normalize non-standard, spontaneous and dialectal Arabic into Modern Standard Arabic. We prove the feasibility of the approach by measuring and comparing machine translation results in terms of BLEU with and without the proposed approach. We show in our tests...

Named entity recognition is an involved task and is one that usually requires the usage of numerous resources. Recognizing Arabic entities is an even more difficult task due to the inherent ambiguity of the Arabic language. Previous approaches that have tackled the problem of Arabic named entity recognition have used Arabic parsers and taggers combined with a huge set of gazetteers and sometime...

This paper describes the parallel development of an Egyptian Arabic Treebank and a morphological analyzer for Egyptian Arabic (CALIMA). By the very nature of Egyptian Arabic, the data collected is informal, for example Discussion Forum text, which we use for the treebank discussed here. In addition, Egyptian Arabic, like other Arabic dialects, is sufficiently different from Modern Standard Arab...

We present QCRI’s Arabic-to-English speech translation system. It features modern web technologies to capture live audio, and broadcasts Arabic transcriptions and English translations simultaneously. Our Kaldi-based ASR system uses the Time Delay Neural Network architecture, while our Machine Translation (MT) system uses both phrase-based and neural frameworks. Although our neural MT system is ...

We develop a framework for using the Natural Language Toolkit (NLTK) to parse Quranic Arabic sentences. This framework supports the construction of a treebank for the Holy Quran. The proposed model succeeds in parsing different Quranic chapters (Suras) in addition to Modern Standard Arabic (MSA) sentences. The availability of such parser will be useful in various natural language processing app...

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 central religious text of Islam. This paper...

This paper presents the study that we have carried out to investigate supervised opinion summarization in Modern Standard Arabic. We use a corpus of news articles. We use conditional random fields (CRF) as machine learning technique. We investigate some features to identify those that allow achieving the best results. Our contribution is to use opinion specific features to summarize Arabic news...

the diwan school is one of the literary schools that emerged in contemporary arabic literature and had a tremendous role in directing arabic literature toward innovation, keeping it in touch with modern literary evolution worldwide in the twentieth century. however, the innovation that this school spread is by large of non-arabic roots and is chiefly based on such foreign literatures as english...