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Selected Works

Yuliya Lierler

Textual Inference

Articles 1 - 3 of 3

Full-Text Articles in Physical Sciences and Mathematics

Model Generation For Generalized Quantifiers Via Answer Set Programming, Yuliya Lierler, Günther Görz Nov 2013

Model Generation For Generalized Quantifiers Via Answer Set Programming, Yuliya Lierler, Günther Görz

Yuliya Lierler

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.


Research Challenges And Opportunities In Knowledge Representation, Section 2.3.2: Applications Based On Formal Models, Natasha Noy, Deborah Mcguinness, Yuliya Lierler Nov 2013

Research Challenges And Opportunities In Knowledge Representation, Section 2.3.2: Applications Based On Formal Models, Natasha Noy, Deborah Mcguinness, Yuliya Lierler

Yuliya Lierler

Final report edited by Natasha Noy and Deborah McGuinness. Report Section 2.3.2, Applications based on formal models, authored by Yuliya Lierer, UNO faculty member.


Logic Programs Vs. First-Order Formulas In Textual Inference, Yuliya Lierler, Vladimir Lifschitz Nov 2013

Logic Programs Vs. First-Order Formulas In Textual Inference, Yuliya Lierler, Vladimir Lifschitz

Yuliya Lierler

In the problem of recognizing textual entailment, the goal is to decide, given a text and a hypothesis expressed in a natural language, whether a human reasoner would call the hypothesis a consequence of the text. One approach to this problem is to use a first-order reasoning tool to check whether the hypothesis can be derived from the text conjoined with relevant background knowledge, after expressing all of them by first-order formulas. Another possibility is to express the hypothesis, the text, and the background knowledge in a logic programming language, and use a logic programming system. We discuss the relation …