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Article
Model Generation for Generalized Quantifiers via Answer Set Programming
Computer Science Faculty Proceedings & Presentations
Document Type
Conference Proceeding
Publication Date
1-1-2006
Disciplines
Abstract
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.
Citation Information
Yuliya Lierler and Günther Görz. "Model Generation for Generalized Quantifiers via Answer Set Programming" (2006) Available at: http://works.bepress.com/yuliya_lierler/28/
8th Conference on Natural Language Processing KOVENS