Physical Sciences and Mathematics Commons^™

Prepositional Phrase Attachment Problem Revisited: How Verbnet Can Help, Dan Bailey, Yuliya Lierler, Benjamin Susman

Yuliya Lierler

The Winograd Schema Challenge And Reasoning About Correlation, Dan Bailey, Amelia Harrison, Yuliya Lierler, Vladimir Lifschitz, Julian Michael

Yuliya Lierler

Aspccgtk: Towards Syntactic Parsing With Semantic Disambiguation, Yuliya Lierler, Peter Schueller

Yuliya Lierler

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

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.

Parsing Combinatory Categorial Grammar With Answer Set Programming: Preliminary Report, Yuliya Lierler, Peter Schüller

Yuliya Lierler

Combinatory categorial grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories to parse a sentence. In this work we propose and implement a new approach to CCG parsing that relies on a prominent knowledge representation formalism, answer set programming (ASP) — a declarative programming paradigm. We formulate the task of CCG parsing as a planning problem and use an ASP computational tool to compute solutions that correspond to valid parses. Compared to other approaches, there is no need to …

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 …

Towards A Tight Integration Of Syntactic Parsing With Semantic Disambiguation By Means Of Declarative Programming, Yuliya Lierler, Peter Schüller

Yuliya Lierler

We propose and advocate the use of an advanced declarative programming paradigm – answer set programming – as a uniform platform for integrated approach towards syntax-semantic processing in natural language. We illustrate that (a) the parsing technology based on answer set programming implementation reaches performance sufficient for being a useful NLP tool, and (b) the proposed method for incorporating semantic information from FRAMENET into syntactic parsing may prove to be useful in allowing semantic-based disambiguation of syntactic structures.

Parsing Combinatory Categorial Grammar Via Planning In Answer Set Programming, Yuliya Lierler, Peter Schueller

Yuliya Lierler