Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
A fully statistical approach to natural language interfaces
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Machine Learning
Learning context-dependent mappings from sentences to logical form
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Dialogue model specification and interpretation for intelligent multimodal HCI
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Specification and evaluation of a Spanish conversational system using dialogue models
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Contextual word spotting in historical manuscripts using Markov logic networks
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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Semantic processing is vital in a dialogue system for the language understanding stage. Recent approaches of semantic processing rely on machine learning methods to perform the task. These are more robust to errors from the speech recogniser. Although these approaches are built on the domain of the dialogue system they do not incorporate contextual information available in the dialogue system. In this paper, we explore the use of contextual information in the form of expectations of a dialogue system to perform semantic processing in a Spoken Dialogue System. We show the benefits on doing so, and propose a Markov Logic model which incorporates such information.