Model Generation for Generalized Quantifiers via Answer Set Programming (2006)
Yuliya Lierler and Guenther Goerz
For the semantic evaluation of natural language sentences, in particular those containing generalized quantifiers, we subscribe to the generate and test methodology to produce models of such sentences. These models are considered as means by which the sentences can be interpreted within a natural language processing system. The goal of this paper is to demonstrate that answer set programming is a simple, efficient and particularly well suited model generation technique for this purpose, leading to a straightforward implementation.
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In 8th Conference on Natural Language Processing (KONVENS) 2006.
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Yuliya Lierler Ph.D. Alumni ylierler [at] unomaha edu