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SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge
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In: DTIC (2013)
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SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Embodiment Challenge
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In: Seventh International Workshop on Semantic Evaluation, June 14-15, 2013. Atlanta, Georgia. (2013)
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Towards Effective Tutorial Feedback for Explanation Questions: A Dataset and Baselines
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In: 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 3-8, 2012. Montreal, Canada. (2012)
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BEETLE II: A System for Tutoring and Computational Linguistics Experimentation
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In: DTIC (2010)
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The Impact of Interpretation Problems on Tutorial Dialogue
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In: DTIC (2010)
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Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues
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In: Callaway, Charles; Campbell, Gwendolyn; Dzikovska, Myroslava; Fallow, Elaine; Moore, Johanna; & Steinhauser, Natalie. (2009). Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues. Proceedings of the Cognitive Science Society, 31(31). Retrieved from: http://www.escholarship.org/uc/item/3xw2x09f (2009)
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| 7 |
Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues
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In: DTIC (2009)
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Linking Semantic and Knowledge Representations in a Multi-Domain Dialogue System
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Linking Semantic and Knowledge Representations in a Multi-Domain Dialogue System
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Abstract:
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.
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URL: https://doi.org/10.1093/logcom/exm067
http://logcom.oxfordjournals.org/cgi/content/short/exm067v1
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Understanding student input for tutorial dialogue in procedural domains
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A Practical Semantic Type Representation for Natural Language Understanding
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