The topics of interest for the workshop session include but are not limited to:
- Infrastructure for the domain: building, adapting and availability of linguistic resources, data sets and tools
- Methods and protocols for data collection
- Acquisition and combination of novel data samples; including digital biomarkers, continuous streaming, monitoring and aggregation of measurements; as well as self-reported behavioral and/or physiological and activity data
- Guidelines, protocols, annotation schemas, annotation tools
- Addressing the challenges of representation, including dealing with data sparsity and dimensionality issues, feature combination from different sources and modalities
- Domain adaptation of NLP/AI tools
- Acoustic/phonetic/phonologic, syntactic, semantic, pragmatic and discourse analysis of data; including modeling of perception (e.g. eye-movement measures of reading) and production processes (e.g. recording of the writing process by means of digital pens, keystroke logging etc.); use of gestures accompanying speech and non-linguistic behavior
- Use of wearable, vision, and ambient sensors or their fusion for detection of cognitive disabilities or decline
- (Novel) Modeling and deep / machine learning approaches for early diagnostics, prediction, monitoring, classification etc. of various cognitive, psychiatric and/or developmental impairments
- Evaluation of the significance of features for screening and diagnostics
- Evaluation of tools, systems, components, metrics, applications and technologies including methodologies making use of NLP; e.g. for predicting clinical scores from (linguistic) features
- Digital platforms/technologies for cognitive assessment and brain training
- Evaluation, comparison and critical assessment of resources
- Involvement of medical/clinical professionals and patients
- Ethical, gender bias and legal questions in research with human data in the domain, and how they can be handled
- Deployment, assessment platforms and services as well as innovative mining approaches that can be translated to practical/clinical applications
- Experiences, lessons learned and the future of NLP/AI in the area