Learning domain-specific grammars from a small number of examples
Paper i proceeding, 2020

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 subgrammar 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.

Sub-grammar Extraction

Constraint Solving

Computational Linguistics


Herbert Lange

Göteborgs universitet

Peter Ljunglöf

Göteborgs universitet

ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence

Vol. 1 422-430
978-989758395-7 (ISBN)

12th International Conference on Agents and Artificial Intelligence, ICAART 2020
Valletta, Malta,


Språkteknologi (språkvetenskaplig databehandling)


Datavetenskap (datalogi)



Mer information

Senast uppdaterat