Foundations of statistical natural language processing
Foundations of statistical natural language processing
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Probabilistic grounding of situated speech using plan recognition and reference resolution
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Automatic learning and generation of social behavior from collective human gameplay
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Report on the first NLG Challenge on Generating Instructions in Virtual Environments (GIVE)
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Semiotic schemas: A framework for grounding language in action and perception
Artificial Intelligence - Special volume on connecting language to the world
Semi-automatic task recognition for interactive narratives with EAT & RUN
Proceedings of the Intelligent Narrative Technologies III Workshop
Talking NPCs in a virtual game world
ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
Training a multilingual sportscaster: using perceptual context to learn language
Journal of Artificial Intelligence Research
Generating referring expressions in context: the GREC task evaluation challenges
Empirical methods in natural language generation
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We use data from a virtual world game for automated learning of words and grammatical constructions and their meanings. The language data are an integral part of the social interaction in the game and consist of chat dialogue, which is only constrained by the cultural context, as set by the nature of the provided virtual environment. Building on previous work, where we extracted a vocabulary for concrete objects in the game by making use of the non-linguistic context, we now target NP/DP grammar, in particular determiners. We assume that we have captured the meanings of a set of determiners if we can predict which determiner will be used in a particular context. To this end we train a classifier that predicts the choice of a determiner on the basis of features from the linguistic and non-linguistic context.