Deep linguistic processing for spoken dialogue systems
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Deep semantic analysis of text
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
No Code Required: Giving Users Tools to Transform the Web
No Code Required: Giving Users Tools to Transform the Web
A linguistic ontology of space for natural language processing
Artificial Intelligence
BEETLE II: a system for tutoring and computational linguistics experimentation
ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
Towards an OWL-based framework for extracting information from clinical texts
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Corpus-based interpretation of instructions in virtual environments
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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We describe a two-layer architecture for supporting semantic interpretation and domain reasoning in dialogue systems. Building system that supports both semantic interpretation and domain reasoning in a transparent and well-integrated manner is an unresolved problem because of the diverging requirements of the semantic representations used in contextual interpretation versus the knowledge representations used in domain reasoning. We propose an architecture that provides both portability and efficiency in natural language interpretation by maintaining separate semantic and domain knowledge representations, and integrating them via an ontology mapping procedure. The ontology mapping is used to obtain representations of utterances in a form most suitable for domain reasoners and to automatically specialize the lexicon. The use of a linguistically motivated parser for producing semantic representations for complex natural language sentences facilitates building portable semantic interpretation components as well as connections with domain reasoners. Two evaluations demonstrate the effectiveness of our approach: we show that a small number of mapping rules are sufficient for customizing the generic semantic representation to a new domain, and that our automatic lexicon specialization technique improves parser speed and accuracy.