International Journal Papers

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Browsing International Journal Papers by Author "Aliane, Hassina"

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    ArA*summarizer: An Arabic text summarization system based on subtopic segmentation and using an A* algorithm for reduction
    (Wiley, 2020-04-19) Bahloul, Belahcene; Aliane, Hassina; Benmohammed, Mohamed
    Automatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster-based, and graph-based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall-oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n-gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works.
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    AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News & Hate Speech Detection Dataset
    (Elsevier, 2021) Hadj Ameur, Mohamed Seghir; Aliane, Hassina
    Along with the COVID-19 pandemic, an "infodemic" of false and misleading information has emerged and has complicated the COVID-19 response efforts. Social networking sites such as Facebook and Twitter have contributed largely to the spread of rumors, conspiracy theories, hate, xenophobia, racism, and prejudice. To combat the spread of fake news, researchers around the world have and are still making considerable efforts to build and share COVID-19 related research articles, models, and datasets. This paper releases "AraCOVID19-MFH"1 a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset. Our dataset contains 10,828 Arabic tweets annotated with 10 different labels. The labels have been designed to consider some aspects relevant to the fact-checking task, such as the tweet’s check worthiness, positivity/negativity, and factuality. To confirm our annotated dataset’s practical utility, we used it to train and evaluate several classification models and reported the obtained results. Though the dataset is mainly designed for fake news detection, it can also be used for hate speech detection, opinion/news classification, dialect identification, and many other tasks.
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    Error Drift Compensation for Data Hiding of the H.264/AVC
    (Romanian Society of Control Engineering and Technical Informatics, 2013) Bouchama, Samira; Hamami, Latifa; Aliane, Hassina
    The error propagation problem is one of the most attractive issues in the field of data hiding of compressed video because the achievement of several data hiding characteristics remains dependant on it. In this paper, a solution to compensate the error propagation is proposed for data hiding of the H.264/AVC. The error compensation is performed by a prior measurement of the introduced error in the watermarked block or in the neighbouring blocks. Two schemes are proposed: The first algorithm exploits the method of watermarking paired-coefficients in each block in order to bring the error to the middle of the block matrix. The distortion caused by each paired-coefficient is calculated in order to give a watermarking priority to the pairs which introduce the minimum error. In the second scheme, the error estimated in the neighbouring blocks is reduced from the residuals during the encoding process. In both schemes, results show that an important improvement of the video quality can be achieved and a good compromise is provided between the video distortion, the bitrate and the embedding.
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    Ontology learning: Grand tour and challenges
    (Elsevier, 2021-02-21) Chérifa Khadir, Ahlem; Aliane, Hassina; Guessoum, Ahmed
    Ontologies are at the core of the semantic web. As knowledge bases, they are very useful resources for many artificial intelligence applications. Ontology learning, as a research area, proposes techniques to automate several tasks of the ontology construction process to simplify the tedious work of manually building ontologies. In this paper we present the state of the art of this field. Different classes of approaches are covered (linguistic, statistical, and machine learning), including some recent ones (deep-learning-based approaches). In addition, some relevant solutions (frameworks), which offer strategies and built-in methods for ontology learning, are presented. A descriptive summary is made to point out the capabilities of the different contributions based on criteria that have to do with the produced ontology components and the degree of automation. We also highlight the challenge of evaluating ontologies to make them reliable, since it is not a trivial task in this field; it actually represents a research area on its own. Finally, we identify some unresolved issues and open questions.