Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graph-based generation of referring expressions
Computational Linguistics
Resolving pronominal reference to abstract entities
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Machine Learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Referring expression generation through attribute-based heuristics
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Incorporating extra-linguistic information into reference resolution in collaborative task dialogue
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Generating referring expressions with reference domain theory
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
REX-J: Japanese referring expression corpus of situated dialogs
Language Resources and Evaluation
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This paper proposes a probabilistic approach to the resolution of referring expressions for task-oriented dialogue systems. The approach resolves descriptions, anaphora, and deixis in a unified manner. In this approach, the notion of reference domains serves an important role to handle context-dependent attributes of entities and references to sets. The evaluation with the REX-J corpus shows promising results.