Orphée is assistant professor of language technology for educational applications. She is mainly interested in how natural language processing techniques can aid computer-assisted language learning. She has great expertise in deep semantic processing, readability prediction and text mining of user-generated content using machine learning techniques.
She obtained her PhD in 2015 for which she devised the first state-of-the-art classification-based readability prediction system for Dutch and the first end-to-end system for fine-grained sentiment analysis. For both systems she investigated the added value of incorporating deep semantic knowledge in the form of coreferential relations, semantic roles and linked open data. During her postdoc, Orphée has been further elaborating her readability research by investigating domain portability and collaborated on papers researching translation quality and post-editing. At the same time, her focus has shifted towards automated writing evaluation: how can we better support (student) writers and teachers using novel techniques from NLP and ML and is it desirable/possible to incorporate more deep semantic knowledge? Her work on sentiment analysis has evolved as well, she explored new techniques for porting her sentiment analysis pipeline to different domains and languages, has successfully finalized a valorization project with the industry and is currently also looking into implicit sentiment analysis and emotion detection.
Orphée is currently co-supervising three PhD-students on the subjects of translatability prediction, transfer learning for emotion detection and event coreference resolution and is actively involved in the interdisciplinary research projects COFI and #NewsDNA.
Orphée is teaching two courses on digital communication, is lecturer-in-charge of the course Computer-Assisted Language Learning and always eager to supervise Bachelor and Master students to help them take their first steps into the wonderful world of Natural Language Processing.