Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
"Dialog Navigator": a question answering system based on large text knowledge base
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Expanding the scope of the ATIS task: the ATIS-3 corpus
HLT '94 Proceedings of the workshop on Human Language Technology
Experiments with interactive question-answering
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A fully-lexicalized probabilistic model for Japanese syntactic and case structure analysis
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Recognizing textual relatedness with predicate-argument structures
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Web-scale distributional similarity and entity set expansion
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Contextualizing semantic representations using syntactically enriched vector models
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A speech understanding system based on statistical representation of semantics
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
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We present a novel scheme of spoken dialogue systems which uses the up-to-date information on the web. The scheme is based on information extraction which is defined by the predicate-argument (P-A) structure and realized by semantic parsing. Based on the information structure, the dialogue system can perform question answering and also proactive information presentation. Feasibility of this scheme is demonstrated with experiments using a domain of baseball news. In order to automatically select useful domain-dependent P-A templates, statistical measures are introduced, resulting to a completely unsupervised learning of the information structure given a corpus. Similarity measures of P-A structures are also introduced to select relevant information. An experimental evaluation shows that the proposed system can make more relevant responses compared with the conventional "bag-of-words" scheme.