Fusion of knowledge-based and data-driven approaches to grammar inductionFusion of knowledge-based and data-driven approaches to grammar induction Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-10-282014enUsing different sources of information for grammar induction results in grammars that vary in coverage and precision. Fusing such grammars with a strategy that exploits their strengths while minimizing their weaknesses is expected to produce grammars with superior performance. We focus on the fusion of grammars produced using a knowledge-based approach using lexicalized ontologies and a data-driven approach using semantic similarity clustering. We propose various algorithms for finding the mapping between the (non-terminal) rules generated by each grammar induction algorithm, followed by rule fusion. Three fusion approaches are investigated: early, mid and late fusion. Results show that late fusion provides the best relative F-measure performance improvement by 20%. http://creativecommons.org/licenses/by/4.0/288-29215th Annual Conference of the International Speech Communication Association Petrakis Evripidis Πετρακης Ευριπιδης Potamianos Alexandros Ποταμιανος Αλεξανδρος Cimiano, Philipp 1977- Walter Sebastian Iosif Ilias Ιωσηφ Ηλιας Unger Christina Georgiladakis Spyridon Γεωργιλαδακης Σπυριδων Chinese and Oriental Languages Information Processing Society Spoken dialogue systems Corpus-based grammar induction Ontology-based grammar induction Grammar fusion