TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Lange, Herbert
A1  - Ljunglöf, Peter
ED  - Rocha, Ana
ED  - Steels, Luc
ED  - van den Herik, Jaap
T1  - Learning domain-specific grammars from a small number of examples
T2  - Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) - Volume 1. February 22-24, 2020, in Valletta, Malta
N2  - In this paper we investigate the problem of grammar inference from a different perspective. The common approach is to try to infer a grammar directly from example sentences, which either requires a large training set or suffers from bad accuracy. We instead view it as a problem of grammar restriction or sub-grammar extraction. We start from a large-scale resource grammar and a small number of examples, and find a sub-grammar that still covers all the examples. To do this we formulate the problem as a constraint satisfaction problem, and use an existing constraint solver to find the optimal grammar. We have made experiments with English, Finnish, German, Swedish and Spanish, which show that 10–20 examples are often sufficient to learn an interesting domain grammar. Possible applications include computer-assisted language learning, domain-specific dialogue systems, computer games, Q/A-systems, and others.
KW  - computational linguistics
KW  - sub-grammar extraction
KW  - constraint solving
KW  - Grammatik
KW  - Beispiel
KW  - Computerlinguistik
KW  - Constraint-Erfüllung
KW  - Fremdsprachenlernen
KW  - Zweisprachigkeit
KW  - Kontrastive Grammatik
Y1  - 2020
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-112109
SN  - 2184-433X
SS  - 2184-433X
SN  - 978-989-758-395-7
SB  - 978-989-758-395-7
U6  - https://doi.org/10.5220/0009371304220430
DO  - https://doi.org/10.5220/0009371304220430
SP  - 422
EP  - 430
PB  - SciTePress
CY  - Setúbal
ER  -