Understanding Natural Language
Understanding Natural Language
Automatic labeling of semantic roles
Computational Linguistics
Support Vector Learning for Semantic Argument Classification
Machine Learning
Semantic role labeling of prepositional phrases
ACM Transactions on Asian Language Information Processing (TALIP)
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Carsim: a system to visualize written road accident reports as animated 3D scenes
TextMean '04 Proceedings of the 2nd Workshop on Text Meaning and Interpretation
Automatic text-to-scene conversion in the traffic accident domain
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Frame semantics in text-to-scene generation
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part IV
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
3D visualization of simple natural language statement using semantic description
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
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In this paper, we propose a machine learning-based NLP system for automatically creating animated storyboards using the action descriptions of movie scripts. We focus particularly on the importance of verb semantics when generating graphics commands, and find that semantic role labelling boosts performance and is relatively robust to the effects of unseen verbs.