Model Generation for Generalized Quantifiers via Answer Set Programming
作者： Yuliya Lierler , Günther Görz
DOI:
关键词: Programming language 、 Mathematics 、 Stable model semantics 、 Well-founded semantics 、 Answer set programming 、 First-generation programming language 、 Programming domain 、 Programming language theory 、 Natural language 、 Semantics (computer science)
摘要: For the semantic evaluation of natural language sentences, in particular those containing generalized quantifiers, we subscribe to generate and test methodology produce models such sentences. These are considered as means by which sentences can be interpreted within a processing system. The goal this paper is demonstrate that answer set programming simple, efficient particularly well suited model generation technique for purpose, leading straightforward implementation.
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