Staff directory

Search for a surname in the box below and then click "Search" to search for Staff. 

If you want to get in touch but you don’t know who to contact, fill in our enquiry form and one of our advisers will get back to you as soon as possible.
4 results

The REAL Corpus: a crowd-sourced corpus of human generated and evaluated spatial references to real-world urban scenes

Conference Proceeding
Bartie, P., Mackaness, W., Gkatzia, D., & Rieser, V. (2016)
The REAL Corpus: a crowd-sourced corpus of human generated and evaluated spatial references to real-world urban scenes. In 10th International Conference on Language Resources and Evaluation (LREC)
We present a newly crowd-sourced data set of natural language references to objects anchored in complex urban scenes (In short: The REAL Corpus – Referring Expressions Anchore...

How to Talk to Strangers: generating medical reports for first time users

Conference Proceeding
Gkatzia, D., Rieser, V., & Lemon, O. (2016)
How to Talk to Strangers: generating medical reports for first time users. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)https://doi.org/10.1109/FUZZ-IEEE.2016.7737739
We propose a novel approach for handling first-time users in the context of automatic report generation from timeseries data in the health domain. Handling first-time users is...

Generating Verbal Descriptions from Medical Sensor Data: A Corpus Study on User Preferences

Conference Proceeding
Gkatzia, D., Rieser, V., Mcsporran, A., Mcgowan, A., Mort, A., & Dewar, M. (2014)
Generating Verbal Descriptions from Medical Sensor Data: A Corpus Study on User Preferences. In BCS Health Informatics Scotland (HIS)
Understanding and interpreting medical sensor data is an essential part of pre-hospital care in medical emergencies, but requires training and previous knowledge. In this pape...

Multi-adaptive Natural Language Generation using Principal Component Regression

Conference Proceeding
Gkatzia, D., Hastie, H., & Lemon, O. (2014)
Multi-adaptive Natural Language Generation using Principal Component Regression. In Proceedings of the 8th International Natural Language Generation Conference, 138-142
We present FeedbackGen, a system that uses a multi-adaptive approach to Natural Language Generation. With the term 'multi-adaptive', we refer to a system that is able to adapt...