Hassina Aliane

Research Center on Scientific and Technical Information | CERIST · Division de R&D en Sciences de l'Information

Machine Translation is an early Artificial Intelligence field of research that has a rich history and a substantial body of literature. If statistical methods dominated over two decades, the success of neural approaches opened up new promises, particularly for languages with rich morphology and limited resources like Arabic. We investigate through...

Word Sense Disambiguation (WSD) is a challenging Natural Language Processing (NLP) task. In the case of the Arabic language, AWSD has its special challenges due to its unique features, such as being agglutinative, lacking diacritics, and having limited annotated resources. This study explores the impact of stop words, including particles, on the pe...

Word Sense Disambiguation (WSD) is a pivotal challenge in natural language processing (NLP), especially for languages with rich morphological features such as Arabic. In this paper, we introduce a novel approach for Arabic WSD by leveraging the power of stacked ensemble models of BERT-based models. Given the scarcity of sense-annotated resources fo...

The vast amount of academic literature being produced has led to academic social networks (ASNs) becoming increasingly important in increasing the visibility of research and facilitating knowledge exchange among researchers worldwide. However, a major challenge for ASNs is the recommendation of relevant content to users, given the overwhelming amou...

Word sense disambiguation is the task of automatically determining the meaning of a polysemous word in a specific context. Word sense induction is the unsupervised clustering of word usages in a different context to distinguish senses and perform unsupervised WSD. Most studies consider function words as stop words and delete them in the pre-process...

The search for relevant scientific articles is a crucial step in any research project. However, the vast number of articles published and available online in digital databases (Google Scholar, Semantic Scholar, etc.) can make this task tedious and negatively impact a researcher's productivity. This article proposes a new method of recommending scie...

Sarcasm is one of the main challenges of sentiment analysis systems. This paper mainly focuses on the recognition of Arabic sarcasm on Twitter. Recognizing sarcasm in tweets is essential for understanding users' opinions on various topics and events. There are only a few attempts regarding saracsm detection in Arabic due to the challenges and compl...

Preservation of cultural heritage is a field of high importance. Recently people are sharing architectural monument images on social media. In this paper, we try to recognize architectural monuments in digital photographs of Algerian cultural heritage using a convolutional neural network (CNN). As no datasets support the diversity of Algerian monum...

We provide in this work, a new approach for automatic bilingual terminology extraction involving Arabic and French languages. We aim to build a bilingual list of terms for a specific field, namely “Algerian Culture”. The approach is based on building a semantic space for context understanding, adopting COALS (Correlated Occurrence Analogue to Lexic...

Academic social networks have become essential online platforms for researchers seeking digital visibility and scientific collaboration. But, the speed with which new scientific articles are published and shared on these academic ecosystems generates a situation of cognitive overload, creating disorientation in the search for relevant and useful in...

Finding relevant scientific articles is a laborious process, which requires a lot of time and effort, especially on online journal databases, where article research is based on metadata such as keywords or authors' names. In this context we are interested in the referencing of articles by topics they address, thanks to topic modeling technology as...

Coronavirus disease (COVID-19) is an infectious respiratory disease that was first discovered in late December 2019, in Wuhan, China, and then spread worldwide causing a lot of panic and death. Users of social networking sites such as Facebook and Twitter have been focused on reading, publishing, and sharing novelties, tweets, and articles regardin...

Along with the COVID-19 pandemic, an “infodemic” of false and misleading information has emerged and has complicated the COVID-19 response e‚orts. 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, res...

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, re...

Nous décrivons dans cet article, une nouvelle approche d’induction de sens des mots pour la langue Arabe dans un espace vectoriel des mots. Les modèles de représentation vectoriels suscitent un grand intérêt de la part de la communauté de recherche TALN. Ces modèles sont fondés sur l’hypothèse distributionnelle qui prend en compte le « contexte » d...

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 pre...

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, re...

Learning ontological relations is an important step on the way to automatically developing ontologies. This paper introduces a novel way to exploit WordNet [16], the combination of pre-trained word embeddings and deep neural networks for the task of ontological relation classification. The data from WordNet and the knowledge encapsulated in the pre...

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 unsupe...

plagiarism is a challenging task in natural language processing, and it has been of interest for many researchers these last years. In this paper we propose the latent semantic approach (LSA) for similarity computation in Arabic documents. Our system shows good performance of LSA method, according to our evaluation and experimentation with high pre...

We describe in this paper a new Arabic word embedding model for word sense induction. Word embedding models are gaining a great interest from the NLP research community and Word2vec is undoubtedly the most influential among these models. These models map all the words of the vocabulary to a vector space and then provide a semantic description of th...

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 dependent 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...

Very few reversible data hiding methods are proposed for compressed video and particularly for the H.264/AVC video codec, despite the importance of both of the watermarking reversibility criterion and the codec. The reversible watermarking techniques of images, when applied to the compressed video, can affect particularly the video quality and bitr...

Classical IR systems are often based on lexical matching using approaches that rely on purely statistical methods founded on distributions of keywords to calculate the similarity between the query and the documents of the corpus. The relevance of a document according to a query is based on the similarity of vocabulary and not according to the thema...

We present in this paper an unsupervised approach to recognize events, time and place expressions in Arabic texts. Arabic is a resource -scarce language and we don't easily have at hand annotated corpora, lexicons and other needed NLP tools. We show in this work that we can recognize events, time and place expressions in Arabic texts without using...

We present in this paper an unsupervised approach to recognize events, time and place expressions in Ara-bic texts. Arabic is a resource-scarce language and we don't easily have at hand annotated corpora, lexicons and other needed NLP tools. We show in this work that we can recognize events, time and place expressions in Arabic texts without using...

Many data hiding algorithms have been proposed for the latest video codec H.264/AVC, most of them are based on the 4×4 luma DCT coefficients. However, for this kind of algorithms, the drift distortion is the main reason which limits the embedding capacity. Few methods have been proposed to compensate or eliminate the error propagation. Though, they...

The semantic web services have been introduced to better describe web services and improve their functioning. Several approaches have been proposed in this field. The most used is OWL-S, where the discovery is to match the user query and the service profile of OWL-S service. In this paper we present a query reformulation approach for searching sema...

We present a new approach to discover Arabic language structures from electronic texts. The method is based on a distributional analysis inspired from Arabic Grammatical Tradition (AGT) and Harris, and uses a minimum knowledge about the Arabic language. The idea underlying this research is that in the absence of a formal model of Arabic language an...

We present in this paper our project to building an ontology centered infrastructure for Arabic resources and applications. The core of this infrastructure is a linguistic ontology that is founded on Arabic Traditional Grammar. The methodology we have chosen consists in reusing an existing ontology, namely the Gold linguistic ontology. We discuss t...

Video watermarking describes the process of embedding information in video to satisfy applications such as the protection of intellectual property and the control of video authentication. In this field, researchers orient their investigations towards the new video standard H264/AVC which is increasingly used because of the coding efficiency it prov...

International audience The work presented here aims to provide a composition model of semantic web services. This model is based on a semantic representation of domain concepts handled by web services, namely, operations and the static concepts used to describe static properties of Web services. Different levels of abstraction are given to the conc...

A Java-Web-Based-Learning Methodology, Case Study : Waterborne diseases The recent advances in web technologies have opened new opportunities for computer-based-education. One can learn independently of time and place constraints, and have instantaneous access to relevant updated material at minimal cost. Several web-based-learning systems have bee...

Today, to effectively meet the needs of users, multilingual information retrieval is an important issue regarding to the continually increasing volume of information in various languages. This paper addresses indexing and retrieval in a trilingual corpus: Arabic, French, English. The proposed system is founded on a knowledge representation formalis...