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SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
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A generative theory of relevance
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IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Dealing with out of domain questions in virtual characters
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Giving instructions in virtual environments by corpus based selection
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Rapid development of advanced question-answering characters by non-experts
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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International Journal of Human-Computer Studies
CL system: giving instructions by corpus based selection
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: user and context diversity - Volume 2
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In this paper, we describe methods for building and evaluation of limited domain question-answering characters. Several classification techniques are tested, including text classification using support vector machines, language-model based retrieval, and cross-language information retrieval techniques, with the latter having the highest success rate. We also evaluated the effect of speech recognition errors on performance with users, finding that retrieval is robust until recognition reaches over 50% WER.