An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
An efficient context-free parsing algorithm
Communications of the ACM
Probabilistic State-Dependent Grammars for Plan Recognition
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Karma: knowledge-based active representations for metaphor and aspect
Karma: knowledge-based active representations for metaphor and aspect
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Grounded semantic composition for visual scenes
Journal of Artificial Intelligence Research
Proceedings of the 11th international conference on Intelligent user interfaces
Spontaneous speech understanding for robust multi-modal human-robot communication
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A dialogue approach to learning object descriptions and semantic categories
Robotics and Autonomous Systems
A Probabilistic Approach to the Interpretation of Spoken Utterances
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Salience-driven Contextual Priming of Speech Recognition for Human-Robot Interaction
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Towards Interpreting Task-Oriented Utterance Sequences
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Towards the interpretation of utterance sequences in a dialogue system
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
A robot learns to know people: first contacts of a robot
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Hypothesis generation and maintenance in the interpretation of spoken utterances
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
The automated understanding of simple bar charts
Artificial Intelligence
Domain Independent Goal Recognition
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Extracting aspects of determiner meaning from dialogue in a virtual world environment
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Probabilistic, multi-staged interpretation of spoken utterances
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Hi-index | 0.00 |
Situated, spontaneous speech may be ambiguous along acoustic, lexical, grammatical and semantic dimensions. To understand such a seemingly difficult signal, we propose to model the ambiguity inherent in acoustic signals and in lexical and grammatical choices using compact, probabilistic representations of multiple hypotheses. To resolve semantic ambiguities we propose a situation model that captures aspects of the physical context of an utterance as well as the speaker's intentions, in our case represented by recognized plans. In a single, coherent Framework for Understanding Situated Speech (FUSS) we show how these two influences, acting on an ambiguous representation of the speech signal, complement each other to disambiguate form and content of situated speech. This method produces promising results in a game playing environment and leaves room for other types of situation models.