Fusion of Knowledge-Based and Data-Driven Approaches to Grammar Induction

Abstract

Georgiladakis S, Unger C, Iosif E, et al. Fusion of Knowledge-Based and Data-Driven Approaches to Grammar Induction. In: Fifteenth Annual Conference of the International Speech Communication Association. 2014.Using 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 map- ping between the (non-terminal) rules generated by each gram- mar 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 per- formance improvement by 20%