WorldCat Identities

Melzi, Soumia

Overview
Works: 2 works in 2 publications in 1 language and 2 library holdings
Roles: Author
Publication Timeline
.
Most widely held works by Soumia Melzi
Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator+( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

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). Contact andon.tchechmedjiev@lirmm.fr Supplementary information Supplementary data are available at Bioinformatics online
Leveraging the dynamics of non-verbal behaviors : modeling social attitude and engagement in human-agent interaction by Soumia Dermouche( )

1 edition published in 2019 in English and held by 1 WorldCat member library worldwide

Social interaction implies exchange between two or more persons, where they adapt their behaviors to each others. With the growing interest in human-agent interactions, it is desirable to make these interactions natural and human like. In this context, we aimed at enhancing the quality of the interaction between users and Embodied Conversational Agents ECAs by (1) endowing the ECA with the capacity to express social attitudes, such as being friendly or dominant depending its role or relationship with its interaction partners; (2) adapting the agent's behavior according to the user's behavior, hence, the conversation partners influence each others through an interaction loop, thus, enhancing the interaction quality; (3) predicting the user's engagement level and adapting the agent's behavior accordingly. We take advantage of the recent advances in machine learning, more specifically, temporal sequence mining and neural networks to model these capacities in the ECA. The first model is used to learn relevant patterns (sequences) of non-verbal signals that best represent attitude variations, and then reproduce them on the agent. The latter is used to encompass the dynamics of non-verbal signals. Two use cases have been explored using the well-known LSTM model: agent's behavior adaptation based on both agent's and user's behavior history, and user's engagement prediction based on his/her own behavior history. The implemented models and algorithms have been validated through a number of perceptive studies as well as through rigorous quantitative analysis of the obtained results. In addition, the realized models have been integrated into a virtual-agent platform
 
Audience Level
0
Audience Level
1
  General Special  
Audience level: 0.94 (from 0.88 for Enhanced f ... to 1.00 for Leveraging ...)

Alternative Names
Melzi, Soumia

Languages