Fehmi Ben Abdesslem

Also published as: Fehmi ben Abdesslem


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Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy
Evangelia Gogoulou | Magnus Boman | Fehmi Ben Abdesslem | Nils Hentati Isacsson | Viktor Kaldo | Magnus Sahlgren
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results.


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Text Categorization for Conflict Event Annotation
Fredrik Olsson | Magnus Sahlgren | Fehmi ben Abdesslem | Ariel Ekgren | Kristine Eck
Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020

We cast the problem of event annotation as one of text categorization, and compare state of the art text categorization techniques on event data produced within the Uppsala Conflict Data Program (UCDP). Annotating a single text involves assigning the labels pertaining to at least 17 distinct categorization tasks, e.g., who were the attacking organization, who was attacked, and where did the event take place. The text categorization techniques under scrutiny are a classical Bag-of-Words approach; character-based contextualized embeddings produced by ELMo; embeddings produced by the BERT base model, and a version of BERT base fine-tuned on UCDP data; and a pre-trained and fine-tuned classifier based on ULMFiT. The categorization tasks are very diverse in terms of the number of classes to predict as well as the skeweness of the distribution of classes. The categorization results exhibit a large variability across tasks, ranging from 30.3% to 99.8% F-score.