$$\mathsf {Uhura}$$ : An Authoring Tool for Specifying Answer-Set Programs Using Controlled Natural Language

2019 ◽  
pp. 559-575
Author(s):  
Tobias Kain ◽  
Hans Tompits
Keyword(s):  
Natural Language ◽  
Authoring Tool ◽  
Controlled Natural Language ◽  
Answer Set

The jobs puzzle: Taking on the challenge via controlled natural language processing

2013 ◽  
Vol 13 (4-5) ◽  
pp. 487-501 ◽  
Author(s):  
ROLF SCHWITTER
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Formal Language ◽  
State Of The Art ◽  
Implicit Information ◽  
Controlled Natural Language ◽  
Problem Description ◽  
High Level ◽  
Answer Set

AbstractIn this paper we take on Stuart C. Shapiro's challenge of solving the Jobs Puzzle automatically and do this via controlled natural language processing. Instead of encoding the puzzle in a formal language that might be difficult to use and understand, we employ a controlled natural language as a high-level specification language that adheres closely to the original notation of the puzzle and allows us to reconstruct the puzzle in a machine-processable way and add missing and implicit information to the problem description. We show how the resulting specification can be translated into an answer set program and be processed by a state-of-the-art answer set solver to find the solutions to the puzzle.

scholarly journals Specifying and Verbalising Answer Set Programs in Controlled Natural Language

2018 ◽  
Vol 18 (3-4) ◽  
pp. 691-705 ◽  
Author(s):  
ROLF SCHWITTER
Keyword(s):  
Natural Language Processing ◽  
Data Structure ◽  
Natural Language ◽  
Language Processing ◽  
Specification Language ◽  
Referring Expressions ◽  
Program Specification ◽  
Controlled Natural Language ◽  
Grammar Rules ◽  
Answer Set

AbstractWe show how a bi-directional grammar can be used to specify and verbalise answer set programs in controlled natural language. We start from a program specification in controlled natural language and translate this specification automatically into an executable answer set program. The resulting answer set program can be modified following certain naming conventions and the revised version of the program can then be verbalised in the same subset of natural language that was used as specification language. The bi-directional grammar is parametrised for processing and generation, deals with referring expressions, and exploits symmetries in the data structure of the grammar rules whenever these grammar rules need to be duplicated. We demonstrate that verbalisation requires sentence planning in order to aggregate similar structures with the aim to improve the readability of the generated specification. Without modifications, the generated specification is always semantically equivalent to the original one; our bi-directional grammar is the first one that allows for semantic round-tripping in the context of controlled natural language processing.

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