MUSE
Machine Understanding for interactive StorytElling
Aims
The MUSE project introduces a new way of exploring and understanding textual information by “bringing text to life” through 3D interactive storytelling. Taking as input natural language text like children’s stories or medical patient education materials, MUSE will process the natural language, translate it into formal knowledge that represents the actions, actors, plots and surrounding world by means of advanced machine learning techniques, and then render these as virtual 3D worlds in which the user can explore through interaction, re-enactment and guided game play.
MUSE’s long-term goal is to enable the innovative text-to-virtual-world translation system to be used in many applications and a variety of domains. Current information communicated to citizens is often very complex making written text difficult to understand. Comparable to the invention of symbolic writing systems several millennia ago, MUSE contributes to a novel symbolic system of communicating natural language utterances.
LIIR is involved in the coordination of the project and in the advanced natural language processing research.
Partners
LIIR coordinates the MUSE project. KU Leuven co-promotors are Prof. Steven Bethard (partially affiliated with the University of Boulder, USA) and Prof. Johan De Tavernier. The other partners are: Jožef Stefan Institute (Prof. Nada Lavrac), Teesside University (Prof. Marc Cavazza), Leiden University (Prof. Paul van den Broek) and Haute Autorité de Santé (Dr. Gersende Georg).Results
We have built an integrated system for semantic role labeling, noun phrase coreference resolution, event classification and temporal relation extraction.
| Period | From 2012-09-01 to 2015-08-31. |
| Financed by | EU FP7-296703 (FET-open call). |
| Supervised by | Marie-Francine Moens |
| Staff | Steven Bethard |
| Wim De Mulder | |
| Quynh Do Thi Ngoc | |
| Oleksandr Kolomiyets | |
| Contact | Marie-Francine Moens |
| More information can be found on the project website http://www.muse-project.eu/ | |
Publications
- KOLOMIYETS, Oleksandr & MOENS, Marie-Francine MotionML: Motion Markup Language: Shallow Approach for Annotating Motions in Text. In Proceedings of Corpus Linguistics. 2013
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