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Dive into the research topics where Amine Abdaoui is active.

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Featured researches published by Amine Abdaoui.


web information systems engineering | 2015

Collaborative Content-Based Method for Estimating User Reputation in Online Forums

Amine Abdaoui; Jérôme Azé; Sandra Bringay; Pascal Poncelet

Collaborative ratings of forum posts have been successfully applied in order to infer the reputations of forum users. Famous websites such as Slashdot or Stack Exchange allow their users to score messages in order to evaluate their content. These scores can be aggregated for each user in order to compute a reputation value in the forum. However, explicit rating functionalities are rarely used in many online communities such as health forums. At the same time, the textual content of the messages can reveal a lot of information regarding the trust that users have in the posted information. In this work, we propose to use these hidden expressions of trust in order to estimate user reputation in online forums.


International Conference on Statistical Language and Speech Processing | 2014

Predicting Medical Roles in Online Health Fora

Amine Abdaoui; Jérôme Azé; Sandra Bringay; Natalia Grabar; Pascal Poncelet

Online health fora are increasingly visited by patients to get help and information related to their health. However, these fora are not limited to patients: a significant number of health professionals actively participate in many discussions. As experts their posted information are very important since, they are able to well explain the problems, the symptoms, correct false affirmations and give useful advices, etc. For someone interested in trusty medical information, obtaining only these kinds of posts can be very useful and informative. Unfortunately, extracting such knowledge needs to navigate over the fora in order to evaluate the information. Navigation and selection are time consuming, tedious, difficult and error-prone activities when done manually. It is thus important to propose a new method for automatically categorize information proposed both by non-experts as well as by professionals in online health fora. In this paper, we propose to use a supervised approach to evaluate what are the most representative components of a post considering vocabularies, uncertainty markers, emotions, misspellings and interrogative forms to perform efficiently this categorization. Experiments have been conducted on two real fora and shown that our approach is efficient for extracting posts done by professionals.


Health Informatics Journal | 2016

Expertise in French health forums

Amine Abdaoui; Jérôme Azé; Sandra Bringay; Natalia Grabar; Pascal Poncelet

More and more health websites hire medical experts (physicians, medical students, experienced volunteers, etc.) and indicate explicitly their medical role in order to notify that they provide high-quality answers. However, medical experts may participate in forum discussions even when their role is not officially indicated. Detecting posts written by medical experts facilitates the quick access to posts that have more chances of being correct and informative. The main objective of this work is to learn classification models that can be used to detect posts written by medical experts in any health forum discussions. Two French health forums have been used to discover the best features and methods for this text categorization task. The obtained results confirm that models learned on appropriate websites may be used efficiently on other websites (more than 98% of F1-measure has been obtained using a Random Forest classifier). A study of misclassified posts highlights the participation of medical experts in forum discussions even if their role is not explicitly indicated.


Bioinformatics | 2018

Enhanced Functionalities for Annotating and Indexing Clinical Text with the NCBO Annotator

Andon Tchechmedjiev; Amine Abdaoui; Vincent Emonet; Soumia Melzi; Jitendra Jonnagaddala; Clement Jonquet

Abstract Summary Second use of clinical data commonly involves annotating biomedical text with terminologies and ontologies. The National Center for Biomedical Ontology Annotator is a frequently used annotation service, originally designed for biomedical data, but not very suitable for clinical text annotation. In order to add new functionalities to the NCBO Annotator without hosting or modifying the original Web service, we have designed a proxy architecture that enables seamless extensions by pre-processing of the input text and parameters, and post processing of the annotations. We have then implemented enhanced functionalities for annotating and indexing free text such as: scoring, detection of context (negation, experiencer, temporality), new output formats and coarse-grained concept recognition (with UMLS Semantic Groups). In this paper, we present the NCBO Annotator+, a Web service which incorporates these new functionalities as well as a small set of evaluation results for concept recognition and clinical context detection on two standard evaluation tasks (Clef eHealth 2017, SemEval 2014). Availability and implementation The Annotator+ has been successfully integrated into the SIFR BioPortal platform—an implementation of NCBO BioPortal for French biomedical terminologies and ontologies—to annotate English text. A Web user interface is available for testing and ontology selection (http://bioportal.lirmm.fr/ncbo_annotatorplus); however the Annotator+ is meant to be used through the Web service application programming interface (http://services.bioportal.lirmm.fr/ncbo_annotatorplus). The code is openly available, and we also provide a Docker packaging to enable easy local deployment to process sensitive (e.g. clinical) data in-house (https://github.com/sifrproject). Supplementary information Supplementary data are available at Bioinformatics online.


Studies in health technology and informatics | 2015

E-Patient reputation in Health Forums

Amine Abdaoui; Jérôme Azé; Sandra Bringay; Pascal Poncelet


international conference on ehealth telemedicine and social medicine | 2014

Patient's rationale: Patient Knowledge retrieval from health forums

Soumia Melzi; Amine Abdaoui; Jérôme Azé; Sandra Bringay; Pascal Poncelet; Florence Galtier


language resources and evaluation | 2017

FEEL: a French Expanded Emotion Lexicon

Amine Abdaoui; Jérôme Azé; Sandra Bringay; Pascal Poncelet


medical informatics europe | 2014

Analysis of Forum Posts Written by Patients and Health Professionals

Amine Abdaoui; Jérôme Azé; Sandra Bringay; Natalia Grabar; Pascal Poncelet


CLEF (Working Notes) | 2017

ICD10 Coding of Death Certificates with the NCBO and SIFR Annotator(s) at CLEF eHealth 2017 Task 1.

Andon Tchechmedjiev; Amine Abdaoui; Vincent Emonet; Clement Jonquet


Actes de l’atelier DEFT de la conférence TALN 2017 | 2017

FrenchSentiClass : un Système Automatisé pour la Classification de Sentiments en Français

Mike Donald Tapi Nzali; Amine Abdaoui; Jérôme Azé; Sandra Bringay; Christian Lavergne; Caroline Mollevi; Pascal Poncelet

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Jérôme Azé

Centre national de la recherche scientifique

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Sandra Bringay

Centre national de la recherche scientifique

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Soumia Melzi

University of Montpellier

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Vincent Emonet

University of Montpellier

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Jitendra Jonnagaddala

University of New South Wales

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