Our group
The LongNLP group is working on developing novel AI methods for capturing longitudinal phenomena in natural language and multi-modal and heterogeneous data. A major focus has been on leveraging linguistic and heterogeneous data from a variety of sources to develop methods for capturing changes in an individual over time (“Creating time sensitive sensors from user-generated language and heterogeneous content”). This research on longitudinal personalized language monitoring places a particular focus on (mental) health use cases. In particular we are developing NLP methods to: (1) represent individuals over time from their language, heterogenous and multi-modal content (2) capture changes in individuals' behaviour over time (3) generate and evaluate synthetic data from individuals' content over time (4) summarise the progress of individuals over time, incorporating information about changes. Our core areas of work include but are not limited to representation learning, temporal modeling, summarisation, generation and evaluation of synthetic language data, model augmentation, transfer learning & personalisation, explainability and rumour verification.
Research Areas
News
Stakeholders
Organisations
Clinicians
Partners