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.

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.

Plagiarism Detection in Medical Research Using Medical Ontology

This paper presents a reference study of available algorithms for plagiarism detection and it develops semantic plagiarism detection algorithm for plagiarism detection in medical research papers by employing the Medical Ontologies available on the World Wide Web. The issue of plagiarism detection in medical research written in natural languages is a complex issue and related exact domain of medical research. There are many used algorithms for plagiarism detection in natural language, which are generally divided into two main categories, the first one is comparison algorithms between files by using fingerprints of files, and files content comparison algorithms, which include strings matching algorithms and text and tree matching algorithms. Recently a lot of research in the field of semantic plagiarism detection algorithms and semantic plagiarism detection algorithms were developed basing of citation analysis models in scientific research. In this research a system for plagiarism detection was developed using “Bing” search engine, where tow type of ontologies used in this system, public ontology as wordNet and many standard international ontologies in medical domain as Diseases ontology which contains a descriptions about diseases and definitions of it and the derivation between diseases.

Plagiarism Detection in Arabic Language using Rhetorical Structure Theory

This paper presents a review of available algorithms and plagiarism detection systems، and an implementation of Plagiarism Detection System using available search engines on the web. Plagiarism detection in natural language documents is a complicated problem and it is related to the characteristics of the language itself. There are many available algorithms for plagiarism detection in natural languages .Generally these algorithms belong to two main categories ; the first one is plagiarism detection algorithms based on fingerprint and the second is plagiarism detection algorithms based on content comparison and includes string matching and tree matching algorithms . Usually available systems of plagiarism detection use specific type of detection algorithms or use a mixture of detection algorithms to achieve effective detection systems (fast and accurate). In this research, a plagiarism detection system has been developed using Bing search engine and a plagiarism detection algorithm based on Rhetorical Structure Theory.

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.

Designing a Compiler for Automatic Content Verification in e-Learning Exams

Proofreading is the process of checking text to detect spelling, grammatical, and semantic errors in order to correct them, proofreading the grammar and the meaning of the natural languages is considered one of the basic objectives for people who are interested in computational linguistics, because it becomes necessary for checking written text on the computers in multiple areas, such as proofreading emails and texts on the websites pages, it is also essential for proofreading scientific articles and researches, and it can be used to correct students' answers in the traditional e-learning exams. In addition to that the manual correction process of students' answers in the traditional way is expensive in terms of time and effort, sometimes it is error prone, and it becomes more difficult when there are large number of students, so the automatic correction process is an important step to save time and effort and it avoids errors during correcting answers in the traditional way. This research presents the stages of building Automatic Content Verification Compiler. It presents the stages of building a system which is interested in English syntax check, and it displays the stages of the lexical analysis which is considered a first stage to execute the syntax analysis, in addition to that it shows the stages of executing the syntax analysis which builds the grammatical model, this model describes the simple sentences in English, the study depends on studying grammatical structure in English, then it suggests suitable parts of this model, and it presents an application which verifies English sentences and draws derivation tree of these sentences.

Towards a system for constructing Arabic Ontology based on natural text

This paper presents ArOntoLearn, a Framework for Arabic Ontology learning from textual resources. Supporting Arabic language and using domain knowledge in the learning process are the main features of our framework. Besides it represents the learned ontology in Probabilistic Ontology Model (POM), which can be translated into any knowledge representation formalism, and implements data-driven change discovery. Therefore it updates the POM according to the corpus changes only, and allows user to trace the evolution of the ontology with respect to the changes in the underlying corpus. Our framework analyses Arabic textual resources, and matches them to Arabic Lexico-syntactic patterns in order to learn new Concepts and Relations. Supporting Arabic language is not that easy task, because current linguistic analysis tools are not efficient enough to process unvocalized Arabic corpuses that rarely contain appropriate punctuation. So we tried to build a flexible and freely configured framework whereas any linguistic analysis tool can be replaced by more sophisticated one whenever it is available.

An Application Oriented Arabic Morphological Analyzer

Morphological analysis is an important step in natural language processing and its various applications. Each kind of these applications needs a certain balance between: performance, accuracy, and generality of solutions (i.e. getting all possible roots); while we focus on performance with a good accuracy in Information retrieval applications, we try to achieve high accuracy in systems like pos-tagger and machine translation, and both high accuracy and high generality in systems like language learning systems and Arabic lexical dictionaries. In this paper, we describe our approach to build a flexible and application oriented Arabic morphological analyzer; this approach is designed to satisfy various requirements of most applications which need morphological processing. It also provides a separate stage (Original Letters Detection Algorithm) which can be plugged easily in any Other morphological analyzer to improve its performance, and with no negative effect on its reliability.

Foundations of Statistical Natural Language Processing

Statistical approaches to processing natural language text have become dominant in recent years. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.