Abstract
The focus of this paper is a question answering system, where the answers are retrieved from a collection of textual documents. The system also includes automatic document summarization and document visualization by means of a semantic graph. The information extracted from the documents is stored as subject-predicate-object triplets, and the indexed terms are expanded using Cyc, a large common sense ontology.
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Dali, L., Rusu, D., Fortuna, B., Mladenić, D., Grobelnik, M. (2010). AnswerArt - Contextualized Question Answering. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15939-8_37
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DOI: https://doi.org/10.1007/978-3-642-15939-8_37
Publisher Name: Springer, Berlin, Heidelberg
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