CERIST DL
CERIST Digital Library is the institutional repository of the Algerian Research Centre on Scientific and Technical Information (CERIST). It provides access to the entire production of CERIST in terms of journal and conference papers, technical and research reports, theses, course materials, etc. Within CERIST DL, you can:
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Recent Submissions
Item
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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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.
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A genetic algorithm feature selection based approach for Arabic Sentiment Classification
(IEEE Computer Society, 2016-11-29) Aliane, Hassina; Aliane, A.A; Ziane, M.; Bensaou, N.
With the recently increasing interest for opinion mining from different research communities, there is an evolving body of work on Arabic Sentiment Analysis. There are few available polarity annotated datasets for this language, so most existing works use these datasets to test the best known supervised algorithms for their objectives. Naïve Bayes and SVM are the best reported algorithms in the Arabic sentiment analysis literature. The work described in this paper shows that using a genetic algorithm to select features and enhancing the quality of the training dataset improve significantly the accuracy of the learning algorithm. We use the LABR dataset of book reviews and compare our results with LABR’s authors’ results.
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Automatic Construction of Ontology from Arabic Texts
(Université Djillali LIABES Sidi-Bel-Abbès, 2012-04-29) Mazari, Ahmed Cherif; Aliane, Hassina; Alimazighi, Zaia
The work which will be presented in this paper is related to the building of an ontology of domain for the Arabic linguistics. We propose an approach of automatic construction that is using statistical techniques to extract elements of ontology from Arabic texts. Among these techniques we use two; the first is the "repeated segment" to identify the relevant terms that denote the concepts associated with the domain and the second is the "co-occurrence" to link these new concepts extracted to the ontology by hierarchical or non- hierarchical relations. The processing is done on a corpus of Arabic texts formed and prepared in advance.
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Distributed Partial Simulation for Graph Pattern Matching
(The Computer Journal, 2022-11-21) Aissam Aouar; Saı̈d Yahiaoui; Lamia Sadeg; Nadia Nouali-Taboudjemat; Kadda Beghdad Bey
Pattern matching in big graphs is important for different modern applications. Recently, this problem was defined in terms of multiple extensions of graph simulation, to reduce complexity and capture more meaningful results. These results were achieved through the relaxation of commonly used constraint in subgraph isomorphism pattern matching. Nevertheless, these graph simulation variant models are still too strict to provide results in many cases, especially when analyzed graphs contain anomalies and incomplete information. To deal with this issue, we introduce a new graph pattern matching (GPM) method, called partial simulation, capable of retrieving matches despite missing parts of the pattern graph, such as vertices and/or edges. Furthermore, considering the number and inequality of the outputs, we define a relevance function to compute a value expressing how each match vertex respects the pattern graph. Similarly, we define partial dual simulation GPM that returns vertices that satisfy a part of the dual simulation constraints and assigns a relevance value to them. Additionally, we provide distributed scalable algorithms to evaluate the proposed partial simulation methods based on the distributed vertex-centric programming paradigm. Finally, our experiments on real-world data graphs demonstrate the effectiveness of the proposed models and the efficiency of their associated algorithms.