Language modelling for a morphologically complex language such as Arabic is a challenging task. Its agglutinative structure results in data sparsity problems and high out-of-vocabulary rates. In this work these problems are tackled by applying the MADA tools to the Arabic text. In addition to morphological decomposition, MADA performs context-dependent stem-normalisation. Thus, if word-level sy...

A chimpanzee (Pan troglodytes), trained to count foods and objects by using Arabic numbers, demonstrated the ability to sum arrays of 0-4 food items placed in 2 of 3 possible sites. To address representational use of numbers, we next baited sites with Arabic numbers as stimuli. In both cases performance was significantly above chance from the first sessions, which suggests that without explicit...

Searching online information is increasingly a daily activity for many people. The multilinguality of online content is also increasing (e.g. the proportion of English web users, which has been decreasing as a fraction the increasing population of web users, dipped below 50% in the summer of 2001). To improve the ability of an English speaker to search mutlilingual content, we built a system th...

This paper describes a novel Arabic Reading Enhancement Tool (ARET) for classroom use, which has been built using corpus-based Natural Language Processing in combination with expert linguistic annotation. The NLP techniques include a widely used morphological analyzer for Modern Standard Arabic to provide word-level grammatical details, and a relational database index of corpus texts to provide...

This paper provides a novel model enhances the Arabic OCR degraded text retrieval effectiveness. The model simulates the Arabic OCR recognition mistakes happens while the recognition process based on word based approach. Then using the expected OCR errors the model expands the user search query. The resulting expanded search query produced higher precision and recall in searching Arabic OCRDegr...

We investigate the problem of learning a part-of-speech (POS) lexicon for a resource-poor language, dialectal Arabic. Developing a high-quality lexicon is often the first step towards building a POS tagger, which is in turn the front-end to many NLP systems. We frame the lexicon acquisition problem as a transductive learning problem, and perform comparisons on three transductive algorithms: Tra...

In this paper, we propose a new semisupervised approach for Arabic word sense disambiguation. Using the corpus and Arabic Wordnet, we define a method to cluster the sentences containing ambiguous words. For each sense, we generate a cluster that we use to construct a semantic tree. Furthermore, we construct a weighted directed graph by matching the tree of the original sentence with semantic tr...

Answering multiple-choice questions, where a set of possible answers is provided together with the question, constitutes a simplified but nevertheless challenging area in question answering research. This paper introduces and evaluates two novel techniques for answer selection. It furthermore analyses in how far performance figures obtained using the English language Web as data source can be t...

This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine...

Example Based Machine Translation (EBMT) is limited by the quantity and scope of its training data. Even with a reasonably large corpus, we will not have examples that cover everything we want to translate. This problem is especially severe in Arabic due to its rich morphology. We demonstrate a novel method that exploits the regular nature of Arabic morphology to increase the quality and covera...