AnswerArt: contextualized question answering

Authors:
Lorand Dali;Delia Rusu;Blaž Fortuna;Dunja Mladenić;Marko Grobelnik
Affiliations:
Jožef Stefan Institute, Department of Knowledge Technologies, Ljubljana, Slovenia;Jožef Stefan Institute, Department of Knowledge Technologies, Ljubljana, Slovenia;Jožef Stefan Institute, Department of Knowledge Technologies, Ljubljana, Slovenia;Jožef Stefan Institute, Department of Knowledge Technologies, Ljubljana, Slovenia;Jožef Stefan Institute, Department of Knowledge Technologies, Ljubljana, Slovenia
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Year:
2010

Citing 3
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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.