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...
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
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...
Bartie, P., Mackaness, W., Gkatzia, D. & Rieser, V. (2015). 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)ISBN 978-2-9517408-9-1
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...
The Scottish Informatics & Computer Science Alliance
Research exchange with the Bielefeld Dialogue Systems group
07 December 2016
07 December 2016
Human Language Technologies have been investigated for a long time and they are now a key element in the information society, becoming more and important along the years. In t...
11 May 2016
11 May 2016
This years post-graduate research conference will be held Wednesday (11th of May) on H-floor.
Presentations and the keynote will be in H5, the poster session, breaks an...
18 January 2016
18 January 2016
The School of Computing recently held a Research day where staff and PhD students shared their expertise in the form of a "One-Minute-Madness". Each of the 68 researchers who ...
Gkatzia, D., Rieser, V., Bartie, P., & Mackaness, W. (2015). From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1936-1942. https://doi.org/10.18653/v1/d15-1224
Predicting the success of referring expressions (RE) is vital for real world applications such as navigation systems. Traditionally, research has focused on studying Referring...
Gkatzia, D., Cercas Curry, A., Rieser, V., & Lemon, O. (2015). A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation. In Proceedings of the 15th European Workshop on Natural Language Generation, 112-113. https://doi.org/10.18653/v1/w15-4720
Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores, such as probabilities. A concrete example of such data is weather data. We w...
Gkatzia, D., & Mahamood, S. (2015). A Snapshot of NLG Evaluation Practices 2005 - 2014. https://doi.org/10.18653/v1/w15-4708
In this paper we present a snapshot of endto-end NLG system evaluations as presented in conference and journal papers1 over the last ten years in order to better understand th...
Cercas Curry, A., Gkatzia, D., & Rieser, V. (2015). Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments. In Proceedings of the 15th European Workshop on Natural Language Generation, 90-94. https://doi.org/10.18653/v1/w15-4715
Referring to landmarks has been identified to lead to improved navigation instructions. However, a previous corpus study suggests that human “wizards” also choose to refer to ...
McGookin, D., Gkatzia, D., & Hastie, H. (2015). Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, 357-361. https://doi.org/10.1145/2785830.2785879
Navigation when running is exploratory, characterised by both starting and ending in the same location, and iteratively foraging the environment to find areas with the most su...
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 Papersdoi: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...
Gkatzia, D., Hastie, H., & Lemon, O. (2014). Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data. In Proceedings of the Conference Volume 1: Long Papers, 1231-1240. https://doi.org/10.3115/v1/p14-1116
We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selec...
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...
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...
Core44, room C44 Merchiston campus
7 December 2016
H5 & H9 Merchiston Campus
10 May 2016
18 January 2016