Liftoff – ReaderBench introduces new online functionalities

International audienceNatural Language Processing (NLP) became a trending domain within recent years for many researches and companies due to its wide applicability and the new advances in technology. The aim of this paper is to introduce an updated version or our open-source NLP framework, ReaderBench (http://readerbench.com/), designed to support both students and tutors in multiple learning scenarios that encompass one or more of the following dimensions: Cohesion Network Analysis of discourse, textual complexity assessment, keywords’ extraction using Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and word2vec semantic models, as well as the analysis of online communities and discussions. The latest version of our ReaderBench framework (v4.1) includes: a) new features, Application Programing Interfaces (APIs) and visualizations (e.g., sociograms, analysis of interaction between participants inside a community), b) a new web interface written in Angular 6, and c) the integration of new technologies to increase performance (i.e., spaCy and AKKA), as well as modularity and ease of deployment (i.e., Artifactory and Maven modules). ReaderBench is a fully functional framework capable to enhance the quality of learning processes conducted in multiple languages (English, French, Romanian, Dutch, Spanish, and Italian), and covering both individual and collaborative assessments

Liftoff – ReaderBench introduces new online functionalities

International audienceNatural Language Processing (NLP) became a trending domain within recent years for many researches and companies due to its wide applicability and the new advances in technology. The aim of this paper is to introduce an updated version or our open-source NLP framework, ReaderBench (http://readerbench.com/), designed to support both students and tutors in multiple learning scenarios that encompass one or more of the following dimensions: Cohesion Network Analysis of discourse, textual complexity assessment, keywords’ extraction using Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and word2vec semantic models, as well as the analysis of online communities and discussions. The latest version of our ReaderBench framework (v4.1) includes: a) new features, Application Programing Interfaces (APIs) and visualizations (e.g., sociograms, analysis of interaction between participants inside a community), b) a new web interface written in Angular 6, and c) the integration of new technologies to increase performance (i.e., spaCy and AKKA), as well as modularity and ease of deployment (i.e., Artifactory and Maven modules). ReaderBench is a fully functional framework capable to enhance the quality of learning processes conducted in multiple languages (English, French, Romanian, Dutch, Spanish, and Italian), and covering both individual and collaborative assessments

ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity

International audienceAssessing textual complexity is a difficult, but important endeavor, especially for adapting learning materials to students' and readers' levels of understanding. With the continuous growth of information technologies spanning through various research fields, automated assessment tools have become reliable solutions to automatically assessing textual complexity. ReaderBench is a text processing framework relying on advanced Natural Language Processing techniques that encompass a wide range of text analysis modules available in a variety of languages, including English, French, Romanian, and Dutch. To our knowledge, ReaderBench is the only open-source multilingual textual analysis solution that provides unified access to more than 200 textual complexity indices including: surface, syntactic, morphological, semantic, and discourse specific factors, alongside cohesion metrics derived from specific lexicalized ontologies and semantic models

Cohesion-Centered Analysis of CSCL Environments using ReaderBench

International audienceComputer Supported Collaborative Learning (CSCL) environments have become a viable learning alternative from which valuable data can be extracted and used for advanced analyses centered on evaluating participants' involvement and their interactions. Such automated assessments are implemented within our ReaderBench framework, a Natural Language Processing platform that contains multiple advanced text analysis functionalities. The ReaderBench framework is based on Cohesion Network Analysis from which different sociograms, relying on semantic similarity, are generated in order to reflect interactions between participants. In this paper, we briefly describe the enforced mechanisms used to analyze three scenarios: individual chat conversations, virtual communities of practice and MOOCs

Development and Functionalization of a Novel Chitosan-Based Nanosystem for Enhanced Drug Delivery

Nowadays, infection diseases are one of the most significant threats to humans all around the world. An encouraging strategy for solving this issue and fighting resistant microorganisms is to develop drug carriers for a prolonged release of the antibiotic to the target site. The purpose of this work was to obtain metronidazole-encapsulated chitosan nanoparticles using an ion gelation route and to evaluate their properties. Due to the advantages of the ionic gelation method, the synthesized polymeric nanoparticles can be applied in various fields, especially pharmaceutical and medical. Loading capacity and encapsulation efficiency varFied depending on the amount of antibiotic in each formulation. Physicochemical characterization using scanning electron microscopy revealed a narrow particle size distribution where 90% of chitosan particles were 163.7 nm in size and chitosan-loaded metronidazole nanoparticles were 201.3 nm in size, with a zeta potential value of 36.5 mV. IR spectra revealed characteristic peaks of the drug and polymer nanoparticles. Cell viability assessment revealed that samples have no significant impact on tested cells. Release analysis showed that metronidazole was released from the chitosan matrix for 24 h in a prolonged course, implying that antibiotic-encapsulated polymer nanostructures are a promising drug delivery system to prevent or to treat various diseases. It is desirable to obtain new formulations based on drugs encapsulated in nanoparticles through different preparation methods, with reduced cytotoxic potential, in order to improve the therapeutic effect through sustained and prolonged release mechanisms of the drug correlated with the reduction of adverse effects