TY  - BOOK
AB  - 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%.
DA  - 2014
KW  - spoken dialogue systems
KW  - corpus-based grammar induction
KW  - ontology-based grammar induction
KW  - grammar fusion
LA  - eng
PY  - 2014
TI  - Fusion of Knowledge-Based and Data-Driven Approaches to Grammar Induction
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27638625
Y2  - 2024-11-22T00:20:07
ER  -