Conceptual graph matching: a flexible algorithm and experiments
Journal of Experimental & Theoretical Artificial Intelligence - Special issue: conceptual graphs workshop
A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
The TREC question answering track
Natural Language Engineering
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Semantics of conceptual graphs
ACL '79 Proceedings of the 17th annual meeting on Association for Computational Linguistics
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ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
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CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
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IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Improving the performance of question answering with semantically equivalent answer patterns
Data & Knowledge Engineering
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Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
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IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
A survey on question answering technology from an information retrieval perspective
Information Sciences: an International Journal
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PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
Answer sentence retrieval by matching dependency paths acquired from question/answer sentence pairs
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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In this paper we present a graph-based approach to question answering. The method assumes a graph representation of question sentences and text sentences. Question answering rules are automatically learnt from a training corpus of questions and answer sentences with the answer annotated. The method is independent from the graph representation formalism chosen. A particular example is presented that uses a specific graph representation of the logical contents of sentences.