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Topics of interest

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 techniques for continuous streaming, monitoring and aggregation; 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 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