Improving Arabic Information Retrieval Results Semantically Using Ontology

This research proposes a new way to improve the search outcome of Arabic semantics by abstractly summarizing the Arabic texts (Abstractive Summary) using natural language processing algorithms(NLP),Word Sense Disambiguation (WSD) and techniques of measuring Semantic Similarity in Arabic WordNet Ontology.

Design a Computerized Lexicon for Machine Translation from Arabic to English

Lexicon plays an essential role in natural language processing systems and specially the machine translation systems, because it provides the system's components with the necessary information for the translation process. Although there have been a number of researches in natural language processing field, not enough attention has been given to the importance of the lexicon and specially the Arabic lexicon.

Measuring the Impact of Readability Features in Fake News Detection

The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.