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...
Natural Language Generation enhances human decision-making with uncertain information.
Conference Proceeding
Gkatzia, D., Lemon, O., & Rieser, V. (2016)
Natural Language Generation enhances human decision-making with uncertain information. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (264-268). https://doi.org/10.18653/v1/P16-2043
Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. We present a comparison of different information presentati...
Finding middle ground? Multi-objective Natural Language Generation from time-series data
Conference Proceeding
Gkatzia, D., Hastie, H., & Lemon, O. (2014)
Finding middle ground? Multi-objective Natural Language Generation from time-series data. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers. https://doi.org/10.3115/v1/e14-4041
A Natural Language Generation (NLG) system is able to generate text from nonlinguistic data, ideally personalising the content to a user’s specific needs. In some cases, howev...
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...