Applying Topic Segmentation Algorithms on Arabic Language

The need of having a topic segmentation system for Arabic text is to improve the functionalities of Arabic Information Retrieval (AIR). Topic segmentation of texts has been used to improve the accuracy of the subsequent processes such as question answering and information retrieval. In this paper, we present the assessment of two algorithms for Arabic text segmentation which are TextTilling and C99. We evaluate the performance of these algorithms using the classical Recall/Precision metrics and the Reader Judgment method

Evaluation of Lexical Cohesion Algorithms for Arabic Topic Segmentation

The need of having a topic segmentation system for Arabic text is due essentially to improve the functionalities of Arabic Information Retrieval (AIR). Topic segmentation of texts has been used to improve the accuracy of the subsequent processes such as question answering and information retrieval. In this paper we present the implementation and the evaluation of two algorithms for Arabic text segmentation which are Text-Tilling and C99. We compare the quality of the outputs of the two algorithms and we evaluate the relative performance of Text Tiling algorithm with respect to another cohesion based segmenter: C99 algorithm using the classical Recall/Precision evaluation metrics and the recently introduced Reader Judgment method.Keywords:Topic Segmentation, Text Tiling algorithm, C99 algorithm, Evaluation, Arabic Language

Adaptive Delivery of Trainings Using Ontologies and Case-Based Reasoning

International audienc

State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework